DUX4_multiomics_master_dataset_analysis
Abby Hill
6/11/2021
Setup
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DIR = 'Y:/DUX4_multiomics/'
RNASEQ_DIR = 'Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/'
RNASEQ_RESULTS_DIR = 'Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/'
RNASEQ_QC_DIR = 'Y:/DUX4_multiomics/DUX4_RNAseq/combine_runs/results/star-salmon-qhqxgg/'
SALMON_DIR = paste0(RNASEQ_RESULTS_DIR,'salmon/')
RNASEQ_ANALYSIS_DIR = 'Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/analysis/'
dir.create(RNASEQ_ANALYSIS_DIR)## Warning in dir.create(RNASEQ_ANALYSIS_DIR): 'Y:
## \DUX4_multiomics\DUX4_RNAseq\combined_caDUX4\results\analysis' already existsdir.create(paste(RNASEQ_ANALYSIS_DIR,'log2FC_pval_tables',sep='/'))## Warning in dir.create(paste(RNASEQ_ANALYSIS_DIR, "log2FC_pval_tables", sep
## = "/")): 'Y:\DUX4_multiomics\DUX4_RNAseq\combined_caDUX4\results\analysis\
## \log2FC_pval_tables' already existsdir.create(paste(RNASEQ_ANALYSIS_DIR,'gene_set_analysis',sep='/'))## Warning in dir.create(paste(RNASEQ_ANALYSIS_DIR, "gene_set_analysis", sep
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## \gene_set_analysis' already existsPROTEOMICS_RESULTS_DIR = 'Y:/DUX4_multiomics/DUX4_MassSpec/Liang_rerun/'
TOTAL_PROTEIN_DIR = 'Y:/DUX4_multiomics/DUX4_MassSpec/Liang_rerun/20210113_Dux4_proteome/outputs/'
PHOSPHOPROTEIN_DIR = 'Y:/DUX4_multiomics/DUX4_MassSpec/Liang_rerun/20210113_DUX4_pSTY/outputs/'
PLOTS_DIR = 'Y:/DUX4_multiomics/Analysis/Plots/'
GSEA_DIR = 'Y:/DUX4_multiomics/Analysis/fGSEA_res/'
MAJIQ_DIR = 'Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/MAJIQ_with_caDUX4/'
updateAnnot = FALSE
run_gsea = FALSE # save time by reading in previously saved results
conditions_to_plot = c('iDUX4dmso','iDUX4t02dox','iDUX4t06dox','iDUX4t14dox')Load QC and Metadata
Load reads, samples, and gene/transcript annotation.
Some QC results are from the initial STAR-salmon run that did not include the caDUX4 sequence in the reference.
# Alignment Metadata
multiqc_picard_RnaSeqMetrics = read.table(file.path(RNASEQ_QC_DIR,'multiqc-align/multiqc_data/multiqc_picard_RnaSeqMetrics.txt'),header=TRUE,stringsAsFactors=FALSE,sep='\t')
multiqc_star = read.table(file.path(RNASEQ_QC_DIR,'multiqc-align/multiqc_data/multiqc_star.txt'),header=TRUE,stringsAsFactors=FALSE,sep='\t')
alignment_metadata = merge(multiqc_picard_RnaSeqMetrics,multiqc_star)
alignment_metadata = alignment_metadata[grep('RSA',alignment_metadata$Sample),]
n_samples = dim(alignment_metadata)[1]
# Read Metadata
multiqc_fastqc = read.table(file.path(RNASEQ_QC_DIR,'multiqc-reads/multiqc_data/multiqc_general_stats.txt'),header=TRUE,stringsAsFactors=FALSE)
multiqc_fastqc_avg=data.frame(sample_name = alignment_metadata$Sample)
multiqc_fastqc_avg$percent_duplicates = rowMeans(cbind(multiqc_fastqc[2*c(1:n_samples)-1,'FastQC_mqc.generalstats.fastqc.percent_duplicates'],multiqc_fastqc[2*c(1:n_samples),'FastQC_mqc.generalstats.fastqc.percent_duplicates']))
multiqc_fastqc_avg$percent_fails = rowMeans(cbind(multiqc_fastqc[2*c(1:n_samples)-1,'FastQC_mqc.generalstats.fastqc.percent_fails'],multiqc_fastqc[2*c(1:n_samples),'FastQC_mqc.generalstats.fastqc.percent_fails']))
multiqc_fastqc_avg$percent_gc = rowMeans(cbind(multiqc_fastqc[2*c(1:n_samples)-1,'FastQC_mqc.generalstats.fastqc.percent_gc'],multiqc_fastqc[2*c(1:n_samples),'FastQC_mqc.generalstats.fastqc.percent_gc']))
# Salmon metadata
salmon_metadata = read.table(file.path(RNASEQ_RESULTS_DIR,'multiqc-salmon/multiqc_data/multiqc_salmon.txt'),header=TRUE,stringsAsFactors=FALSE,sep='\t')
# combine QC data
mapping_metrics = cbind(alignment_metadata, multiqc_fastqc_avg[,2:4],salmon_metadata[,2:dim(salmon_metadata)[2]])
# Sample metadata
RNA_sample_metadata = read_delim(paste(RNASEQ_DIR,'Sample_Metadata.txt',sep='/'),delim='\t')## Rows: 24 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): Name, Biospecimen
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.RNA_sample_metadata$treatment = gsub('t00','dmso',unlist(strsplit(RNA_sample_metadata$Biospecimen,' '))[c(1:n_samples)*3-1])
RNA_sample_metadata$treatment = gsub('t02','t02dox',RNA_sample_metadata$treatment)
RNA_sample_metadata$treatment = gsub('t06','t06dox',RNA_sample_metadata$treatment)
RNA_sample_metadata$treatment = gsub('t14','t14dox',RNA_sample_metadata$treatment)
RNA_sample_metadata$cells = unlist(strsplit(RNA_sample_metadata$Biospecimen,' '))[c(1:n_samples)*3-2]
RNA_sample_metadata$cells[RNA_sample_metadata$cells == 'WT'] = '0WT'
RNA_sample_metadata$condition = paste(RNA_sample_metadata$cells,RNA_sample_metadata$treatment,sep='')
colnames(RNA_sample_metadata) = gsub(' ','_',tolower(colnames(RNA_sample_metadata)),fixed=TRUE)
RNA_sample_metadata$biospecimen = gsub('WT','0WT',as.character(RNA_sample_metadata$biospecimen))
colnames(RNA_sample_metadata)[1] = 'sample_name'
RNA_sample_metadata$biospecimen = gsub('t00','dmso',RNA_sample_metadata$biospecimen)
RNA_sample_metadata## # A tibble: 24 × 5
## sample_name biospecimen treatment cells condition
## <chr> <chr> <chr> <chr> <chr>
## 1 RSA0000107803_RSA0000107683 iDUX4 t06 rep1 t06dox iDUX4 iDUX4t06dox
## 2 RSA0000107804_RSA0000107684 iDUX4 t06 rep2 t06dox iDUX4 iDUX4t06dox
## 3 RSA0000107805_RSA0000107685 iDUX4 t06 rep3 t06dox iDUX4 iDUX4t06dox
## 4 RSA0000107806_RSA0000107686 iDUX4 t06 rep4 t06dox iDUX4 iDUX4t06dox
## 5 RSA0000107807_RSA0000107687 iDUX4 t14 rep1 t14dox iDUX4 iDUX4t14dox
## 6 RSA0000107808_RSA0000107688 iDUX4 t14 rep2 t14dox iDUX4 iDUX4t14dox
## 7 RSA0000107809_RSA0000107689 iDUX4 t14 rep3 t14dox iDUX4 iDUX4t14dox
## 8 RSA0000107810_RSA0000107690 iDUX4 t14 rep4 t14dox iDUX4 iDUX4t14dox
## 9 RSA0000107811_RSA0000107691 0WT dmso rep1 dmso 0WT 0WTdmso
## 10 RSA0000107812_RSA0000107692 0WT dmso rep2 dmso 0WT 0WTdmso
## # … with 14 more rowsMS_sample_metadata = read.table(paste0(TOTAL_PROTEIN_DIR,'/annotation_with_channels.csv'),sep=',',header=TRUE)
MS_sample_metadata = unique(MS_sample_metadata[,c('biospecimenName','BioRep','Channel','Exp')])
MS_n_samples = dim(MS_sample_metadata)[1]
MS_sample_metadata$sample = gsub('_','',MS_sample_metadata$biospecimenName)
MS_sample_metadata$sample = gsub('t00','dmso',MS_sample_metadata$sample)
MS_sample_metadata$sample = gsub('t02','t02dox',MS_sample_metadata$sample)
MS_sample_metadata$sample = gsub('t06','t06dox',MS_sample_metadata$sample)
MS_sample_metadata$sample = gsub('t14','t14dox',MS_sample_metadata$sample)
MS_sample_metadata$condition = gsub('_','',MS_sample_metadata$Exp)
MS_sample_metadata$condition = gsub('t00','dmso',MS_sample_metadata$condition)
MS_sample_metadata$condition = gsub('t02','t02dox',MS_sample_metadata$condition)
MS_sample_metadata$condition = gsub('t06','t06dox',MS_sample_metadata$condition)
MS_sample_metadata$condition = gsub('t14','t14dox',MS_sample_metadata$condition)
MS_sample_metadata$cells = unlist(strsplit(MS_sample_metadata$biospecimenName,'_'))[c(1:MS_n_samples)*3-2]
MS_sample_metadata## biospecimenName BioRep Channel Exp sample condition
## 1 iDUX4_t00_BR1 iDUX4_t00_BR1 126 iDUX4_t00 iDUX4dmsoBR1 iDUX4dmso
## 2 iDUX4_t00_BR2 iDUX4_t00_BR2 127_N iDUX4_t00 iDUX4dmsoBR2 iDUX4dmso
## 3 iDUX4_t00_BR3 iDUX4_t00_BR3 127_C iDUX4_t00 iDUX4dmsoBR3 iDUX4dmso
## 4 iDUX4_t02_BR1 iDUX4_t02_BR1 128_N iDUX4_t02 iDUX4t02doxBR1 iDUX4t02dox
## 5 iDUX4_t02_BR2 iDUX4_t02_BR2 128_C iDUX4_t02 iDUX4t02doxBR2 iDUX4t02dox
## 6 iDUX4_t02_BR3 iDUX4_t02_BR3 129_N iDUX4_t02 iDUX4t02doxBR3 iDUX4t02dox
## 7 iDUX4_t06_BR1 iDUX4_t06_BR1 129_C iDUX4_t06 iDUX4t06doxBR1 iDUX4t06dox
## 8 iDUX4_t06_BR2 iDUX4_t06_BR2 130_N iDUX4_t06 iDUX4t06doxBR2 iDUX4t06dox
## 9 iDUX4_t06_BR3 iDUX4_t06_BR3 130_C iDUX4_t06 iDUX4t06doxBR3 iDUX4t06dox
## 10 iDUX4_t14_BR1 iDUX4_t14_BR1 131_N iDUX4_t14 iDUX4t14doxBR1 iDUX4t14dox
## 11 iDUX4_t14_BR2 iDUX4_t14_BR2 131_C iDUX4_t14 iDUX4t14doxBR2 iDUX4t14dox
## 12 iDUX4_t14_BR3 iDUX4_t14_BR3 132_N iDUX4_t14 iDUX4t14doxBR3 iDUX4t14dox
## 13 WT_t00_BR1 WT_t00_BR1 132_C WT_t00 WTdmsoBR1 WTdmso
## 14 WT_t00_BR2 WT_t00_BR2 133_N WT_t00 WTdmsoBR2 WTdmso
## 15 WT_t14_BR1 WT_t14_BR1 133_C WT_t14 WTt14doxBR1 WTt14dox
## 16 WT_t14_BR2 WT_t14_BR2 134_N WT_t14 WTt14doxBR2 WTt14dox
## cells
## 1 iDUX4
## 2 iDUX4
## 3 iDUX4
## 4 iDUX4
## 5 iDUX4
## 6 iDUX4
## 7 iDUX4
## 8 iDUX4
## 9 iDUX4
## 10 iDUX4
## 11 iDUX4
## 12 iDUX4
## 13 WT
## 14 WT
## 15 WT
## 16 WTcontrasts = read.table(paste(DIR,'Contrasts.txt',sep='/'),sep='\t',header=TRUE)
all_model_contrasts = contrasts[,2:7]
contrasts_of_interest = contrasts[c(1,2,4:6,16),]
model_contrasts_of_interest = contrasts_of_interest[,2:7]
# Gene/Transcript metadata/annotation
salmon_transcript_ids = read.table(paste(SALMON_DIR,list.files(SALMON_DIR)[1],'quant.sf',sep='/'),stringsAsFactors=FALSE,header=TRUE)[,1]
if(updateAnnot==TRUE){
ensembl_v98_human <- useMart(host='sep2019.archive.ensembl.org',biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl')
biomartTranscriptAnnot = getBM(attributes=c('ensembl_transcript_id','ensembl_transcript_id_version','transcript_length','transcript_biotype','transcript_count','ensembl_gene_id','ensembl_gene_id_version','external_gene_name','gene_biotype'),filters=c('ensembl_transcript_id_version'),values=salmon_transcript_ids,mart=ensembl_v98_human)
write.table(biomartTranscriptAnnot,'Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartTranscriptAnnot.txt',sep='\t')
biomartGeneAnnot = getBM(attributes=c('ensembl_gene_id','ensembl_gene_id_version','external_gene_name','gene_biotype','strand','chromosome_name','start_position','end_position','band','description'),filters=c('ensembl_gene_id'),values=unique(biomartTranscriptAnnot$ensembl_gene_id),mart=ensembl_v98_human)
write.table(biomartGeneAnnot,'Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartGeneAnnot.txt',sep='\t')
biomartProteinAnnot = getBM(attributes=c('hgnc_symbol','uniprotswissprot','ensembl_gene_id'),filters=c('ensembl_gene_id'),values=unique(biomartTranscriptAnnot$ensembl_gene_id),mart=ensembl_v98_human)
write.table(biomartProteinAnnot,'Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartProteinAnnot.txt',row.names=FALSE,sep='\t')
} else {
biomartTranscriptAnnot = read.table('Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartTranscriptAnnot.txt',sep='\t',header=TRUE,stringsAsFactors=FALSE)
biomartGeneAnnot = read.table('Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartGeneAnnot.txt',sep='\t',header=TRUE,stringsAsFactors=FALSE)
biomartProteinAnnot = read.table('Y:/Resources_Abby/Annotation/hsapiens/ensembl_98/biomartProteinAnnot.txt',sep='\t',header=TRUE,stringsAsFactors=FALSE)
}
biomartProteinAnnot_uniprot_select = biomartProteinAnnot[biomartProteinAnnot$uniprotswissprot != '',]
biomart_tx2gene = unique(biomartTranscriptAnnot[,c('ensembl_transcript_id_version','ensembl_gene_id')])
# Add in the sequence for the dox-inducible caDUX4 gene
biomart_tx2gene_caDUX4 = rbind(biomart_tx2gene,c('caDUX4_sequence','caDUX4_sequence'))
caDUX4_geneAnnot = biomartGeneAnnot[biomartGeneAnnot$external_gene_name=='DUX4',]
caDUX4_geneAnnot[1,] = c('caDUX4_sequence','caDUX4_sequence','caDUX4','inducible_protein_coding','NA','NA','NA','NA','NA','inducible codon altered DUX4')
biomartGeneAnnot = rbind(biomartGeneAnnot,caDUX4_geneAnnot)Total Proteomics
Read in data
Total protein protein-level analysis (from Liang Xue’s pipeline based on MSstats)
total_protein_quantwide = read.table(paste(TOTAL_PROTEIN_DIR,'q09_quantwide_rbiname_protein_prname.txt',sep='/'),header=TRUE)
total_protein_comparewide = read.table(paste(TOTAL_PROTEIN_DIR,'DUX4_added_columns/q10_comparewide_protein_prname.txt',sep='/'),header=TRUE)
correct_condition_labels <- function(data_colnames){
data_colnames = gsub('_t','t',data_colnames)
data_colnames = gsub('_BR','BR',data_colnames)
data_colnames = gsub('.','_',data_colnames,fixed=TRUE)
data_colnames = gsub('t00','dmso',data_colnames,fixed=TRUE)
data_colnames = gsub('t02','t02dox',data_colnames,fixed=TRUE)
data_colnames = gsub('t06','t06dox',data_colnames,fixed=TRUE)
data_colnames = gsub('t14','t14dox',data_colnames,fixed=TRUE)
}
colnames(total_protein_quantwide) = correct_condition_labels(colnames(total_protein_quantwide))
colnames(total_protein_comparewide) = correct_condition_labels(colnames(total_protein_comparewide))
total_protein_merged = merge(total_protein_quantwide,total_protein_comparewide,by.x='ProteinGroup',by.y='ProteinGroup',all.x=TRUE,all.y=TRUE)
duplicated_proteins = names(table(total_protein_merged$Protein)[table(total_protein_merged$Protein)>1])
ms_comparisons = gsub('log2FC_','',colnames(total_protein_comparewide)[grep('log2FC',colnames(total_protein_comparewide))])
total_protein_comparisons = list()
for(curr_contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[curr_contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
total_protein_comparisons[[curr_contrast]] = total_protein_comparewide[,c(1:4,grep(curr_contrast,colnames(total_protein_comparewide)))]
colnames(total_protein_comparisons[[curr_contrast]]) = gsub(paste0('_',curr_contrast),'',colnames(total_protein_comparisons[[curr_contrast]]))
# total_protein_comparisons[[curr_contrast]]$reference_mean = rowMeans(total_protein_quantwide[,grep(curr_reference,colnames(total_protein_quantwide))])
}Exploratory data analysis: PCA on total proteomics data
total_protein_quantwide_for_PCA = total_protein_quantwide[,grep('BR',colnames(total_protein_quantwide))]
total_protein_quantwide_for_PCA = total_protein_quantwide_for_PCA[rowSums(is.na(total_protein_quantwide_for_PCA))==0,]
PCAres_total_protein_0 = prcomp(t(total_protein_quantwide_for_PCA),center=TRUE,scale=TRUE)
PCAres_total_protein = merge(as.data.frame(PCAres_total_protein_0$x), as.data.frame(MS_sample_metadata),by.x=0,by.y='sample')
percentVar_total_protein = round(100 * summary(PCAres_total_protein_0)$importance[2,])
ggplot(PCAres_total_protein,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('total proteomics data, centered and scaled') +
xlab(paste0("PC1: ",percentVar_total_protein[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_total_protein[2],"% variance"))
PCAres_total_protein_0 = prcomp(t(total_protein_quantwide_for_PCA),center=TRUE,scale=FALSE)
PCAres_total_protein = merge(as.data.frame(PCAres_total_protein_0$x), as.data.frame(MS_sample_metadata),by.x=0,by.y='sample')
percentVar_total_protein = round(100 * summary(PCAres_total_protein_0)$importance[2,])
ggplot(PCAres_total_protein,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('total proteomics data, centered NOT scaled') +
xlab(paste0("PC1: ",percentVar_total_protein[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_total_protein[2],"% variance"))
Total protein volcano plots
ms_total_protein_condition_means = as.data.frame(matrix(0,nrow=dim(total_protein_quantwide)[1],ncol=length(unique(MS_sample_metadata$condition))))
colnames(ms_total_protein_condition_means) = gsub('0WT','WT',unique(MS_sample_metadata$condition))
for(curr_condition in gsub('0WT','WT',unique(MS_sample_metadata$condition))){
ms_total_protein_condition_means[,curr_condition] = log2(rowMeans(2^total_protein_quantwide[,grep(curr_condition,colnames(total_protein_quantwide))]))
}
colnames(ms_total_protein_condition_means) = gsub('0WT','WT',colnames(ms_total_protein_condition_means))
ms_total_protein_conditions_contrasts = list()
for(contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
curr_condition_means = ms_total_protein_condition_means[,c(curr_numerator,curr_reference)]
colnames(curr_condition_means) = c('numerator_mean','reference_mean')
ms_total_protein_conditions_contrasts[[contrast_idx]] = merge(total_protein_comparewide[,c(1:4,grep(curr_contrast,colnames(total_protein_comparewide)))],curr_condition_means,by.x=0,by.y=0,all.x=TRUE,all.y=FALSE)
ms_total_protein_conditions_contrasts[[contrast_idx]] = merge(ms_total_protein_conditions_contrasts[[contrast_idx]],biomartProteinAnnot,by.x='Protein',by.y='uniprotswissprot',all.x=TRUE,all.y=FALSE)
colnames(ms_total_protein_conditions_contrasts[[contrast_idx]])[grep('log2FC',colnames(ms_total_protein_conditions_contrasts[[contrast_idx]]))] = 'log2FC'
colnames(ms_total_protein_conditions_contrasts[[contrast_idx]])[grep('adj_p',colnames(ms_total_protein_conditions_contrasts[[contrast_idx]]))] = 'adj_pvalue'
}
names(ms_total_protein_conditions_contrasts) = ms_comparisons
make_save_contrast_volcano_plots <- function(contrasts_data,dir,color_by='reference_mean',color_trans='identity',legend_title='reference level CPM'){
dir.create(dir,recursive=TRUE,showWarnings=FALSE)
log2FC_lim = max(max(unlist(lapply(contrasts_data,function(x) max(x$log2FC,na.rm=TRUE)))),-1*min(unlist(lapply(contrasts_data,function(x) min(x$log2FC,na.rm=TRUE)))))
max_neg_log_adj_p = max(unlist(lapply(contrasts_data,function(x) max(-log10(x$adj_pvalue),na.rm=TRUE))))
for(curr_contrast_idx in 1:length(contrasts_data)){
curr_contrast = names(contrasts_data)[curr_contrast_idx]
print(ggplot(contrasts_data[[curr_contrast]],aes(x=log2FC,y=-log10(adj_pvalue),color=get(color_by))) + geom_point(alpha=1,shape=16,size=2) + ggtitle(paste('condition contrasts model:',curr_contrast)) + coord_cartesian(xlim=log2FC_lim*c(-1.05,1.05),ylim=c(0,1.05*max_neg_log_adj_p)) + scale_color_gradient(trans=color_trans,low="#a1dab4",high="#253494",space="Lab",na.value="grey50",guide="colourbar",aesthetics="color",name=legend_title) + xlab('log2(fold change)') + ylab('-log10(adjusted p-value)') + theme_bw())
ggsave(paste(dir,'/',curr_contrast,'.pdf',sep=''),device='pdf',width=10,height=8)
}
}
make_save_contrast_volcano_plots(ms_total_protein_conditions_contrasts,dir=paste0(PLOTS_DIR,'MS_total_protein/'),legend_title='reference level abundance')## Warning: Removed 2 rows containing missing values (geom_point).
## Removed 2 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_point).
## Plot selected proteins
total_protein_to_plot = as.data.frame(t(total_protein_quantwide[,grep('BR',colnames(total_protein_quantwide))]))
colnames(total_protein_to_plot) = total_protein_quantwide$ProteinGroup
row.names(total_protein_to_plot) == MS_sample_metadata$sample## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUEtotal_protein_to_plot$Sample = MS_sample_metadata$sample
total_protein_to_plot$Condition = MS_sample_metadata$condition
total_protein_to_plot$Cells = MS_sample_metadata$cells
plot_protein_normalized_abundance <- function(curr_gene_symbol){
curr_protein_id = biomartProteinAnnot$uniprotswissprot[biomartProteinAnnot$hgnc_symbol==curr_gene_symbol]
if(is.na(curr_protein_id)){
message('protein id not found')
} else {
ggplot(total_protein_to_plot,aes_string(x='Condition', y=curr_protein_id, color='Condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90),legend.title=element_text('Condition')) +
stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
labs( title = paste(curr_gene_symbol,' (',curr_protein_id,')',sep='')) +
ylab(paste(curr_protein_id,' normalized abundance'))
}
}
plot_protein_normalized_abundance('ZSCAN4')## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot_protein_normalized_abundance('DUXA')## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot_protein_normalized_abundance('PRAMEF12')## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
Peptide-level data
read in and format total proteomics peptide-level data, make and save volcano plots for all available comparisons
total_protein_quantwide_peptides = read.table(paste(TOTAL_PROTEIN_DIR,'q09_quantwide_rbiname_peptide_prname.txt',sep='/'),header=TRUE)
total_protein_comparewide_peptides = read.table(paste(TOTAL_PROTEIN_DIR,'q10_comparewide_peptide_prname.txt',sep='/'),header=TRUE)
colnames(total_protein_comparewide_peptides) = correct_condition_labels(colnames(total_protein_comparewide_peptides))
colnames(total_protein_quantwide_peptides) = correct_condition_labels(colnames(total_protein_quantwide_peptides))
total_protein_peptide_comparisons = list()
for(curr_contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[curr_contrast_idx]
total_protein_peptide_comparisons[[curr_contrast]] = total_protein_comparewide_peptides[,c(1:3,grep(curr_contrast,colnames(total_protein_comparewide_peptides)))]
colnames(total_protein_peptide_comparisons[[curr_contrast]]) = gsub(paste0('_',curr_contrast),'',colnames(total_protein_peptide_comparisons[[curr_contrast]]))
}
ms_total_protein_peptide_condition_means = as.data.frame(matrix(0,nrow=dim(total_protein_quantwide_peptides)[1],ncol=length(unique(MS_sample_metadata$condition))))
colnames(ms_total_protein_peptide_condition_means) = gsub('0WT','WT',unique(MS_sample_metadata$condition))
for(curr_condition in gsub('0WT','WT',unique(MS_sample_metadata$condition))){
ms_total_protein_peptide_condition_means[,curr_condition] = log2(rowMeans(2^total_protein_quantwide_peptides[,grep(curr_condition,colnames(total_protein_quantwide_peptides))]))
}
colnames(ms_total_protein_peptide_condition_means) = gsub('0WT','WT',colnames(ms_total_protein_peptide_condition_means))
ms_total_protein_peptide_conditions_contrasts = list()
for(contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
curr_condition_means = ms_total_protein_peptide_condition_means[,c(curr_numerator,curr_reference)]
colnames(curr_condition_means) = c('numerator_mean','reference_mean')
ms_total_protein_peptide_conditions_contrasts[[contrast_idx]] = merge(total_protein_comparewide_peptides[,c(1:4,grep(curr_contrast,colnames(total_protein_comparewide_peptides)))],curr_condition_means,by.x=0,by.y=0,all.x=TRUE,all.y=FALSE)
ms_total_protein_peptide_conditions_contrasts[[contrast_idx]] = merge(ms_total_protein_peptide_conditions_contrasts[[contrast_idx]],biomartProteinAnnot,by.x='Protein',by.y='uniprotswissprot',all.x=TRUE,all.y=FALSE)
colnames(ms_total_protein_peptide_conditions_contrasts[[contrast_idx]])[grep('log2FC',colnames(ms_total_protein_peptide_conditions_contrasts[[contrast_idx]]))] = 'log2FC'
colnames(ms_total_protein_peptide_conditions_contrasts[[contrast_idx]])[grep('adj_p',colnames(ms_total_protein_peptide_conditions_contrasts[[contrast_idx]]))] = 'adj_pvalue'
}
names(ms_total_protein_peptide_conditions_contrasts) = ms_comparisons
make_save_contrast_volcano_plots(ms_total_protein_peptide_conditions_contrasts,dir=paste0(PLOTS_DIR,'MS_total_protein_peptides/'),legend_title='reference level abundance')## Warning: Removed 7 rows containing missing values (geom_point).
## Removed 7 rows containing missing values (geom_point).
## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing missing values (geom_point).## Warning: Removed 19 rows containing missing values (geom_point).
Phospho-proteomics
Read in data
Read in and format phospho-peptide data, make and save volcano plots
phospho_quantwide_peptides = read.table(paste(PHOSPHOPROTEIN_DIR,'q09_quantwide_rbiname_peptide_prname.txt',sep='/'),header=TRUE)
phospho_comparewide_peptides = read.table(paste(PHOSPHOPROTEIN_DIR,'q10_comparewide_peptide_prname.txt',sep='/'),header=TRUE)
colnames(phospho_comparewide_peptides) = correct_condition_labels(colnames(phospho_comparewide_peptides))
colnames(phospho_quantwide_peptides) = correct_condition_labels(colnames(phospho_quantwide_peptides))
phospho_comparewide_peptides$Peptide = unlist(lapply(phospho_comparewide_peptides$PeptideSequence,function(x) toupper(strsplit(x,'|',fixed=TRUE)[[1]][1])))
phospho_comparewide_peptides$Residue.Both = unlist(lapply(phospho_comparewide_peptides$PeptideSequence,function(x) gsub('(Phospho)','',(strsplit(x,'|',fixed=TRUE)[[1]][2]),fixed=TRUE)))
phospho_peptide_comparisons = list()
for(curr_contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[curr_contrast_idx]
phospho_peptide_comparisons[[curr_contrast]] = phospho_comparewide_peptides[,c(1:3,grep(curr_contrast,colnames(phospho_comparewide_peptides)))]
colnames(phospho_peptide_comparisons[[curr_contrast]]) = gsub(paste0('_',curr_contrast),'',colnames(phospho_peptide_comparisons[[curr_contrast]]))
}
ms_phospho_peptide_condition_means = as.data.frame(matrix(0,nrow=dim(phospho_quantwide_peptides)[1],ncol=length(unique(MS_sample_metadata$condition))))
colnames(ms_phospho_peptide_condition_means) = gsub('0WT','WT',unique(MS_sample_metadata$condition))
for(curr_condition in gsub('0WT','WT',unique(MS_sample_metadata$condition))){
ms_phospho_peptide_condition_means[,curr_condition] = log2(rowMeans(2^phospho_quantwide_peptides[,grep(curr_condition,colnames(phospho_quantwide_peptides))]))
}
colnames(ms_phospho_peptide_condition_means) = gsub('0WT','WT',colnames(ms_phospho_peptide_condition_means))
ms_phospho_peptide_conditions_contrasts = list()
for(contrast_idx in 1:length(ms_comparisons)){
curr_contrast = ms_comparisons[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
curr_condition_means = ms_phospho_peptide_condition_means[,c(curr_numerator,curr_reference)]
colnames(curr_condition_means) = c('numerator_mean','reference_mean')
ms_phospho_peptide_conditions_contrasts[[contrast_idx]] = merge(phospho_comparewide_peptides[,c(1:4,grep(curr_contrast,colnames(phospho_comparewide_peptides)),dim(phospho_comparewide_peptides)[2]-1,dim(phospho_comparewide_peptides)[2])],curr_condition_means,by.x=0,by.y=0,all.x=TRUE,all.y=FALSE)
ms_phospho_peptide_conditions_contrasts[[contrast_idx]]$Row.names=NULL
ms_phospho_peptide_conditions_contrasts[[contrast_idx]] = merge(ms_phospho_peptide_conditions_contrasts[[contrast_idx]],biomartProteinAnnot,by.x='Protein',by.y='uniprotswissprot',all.x=TRUE,all.y=FALSE)
colnames(ms_phospho_peptide_conditions_contrasts[[contrast_idx]])[grep('log2FC',colnames(ms_phospho_peptide_conditions_contrasts[[contrast_idx]]))] = 'log2FC'
colnames(ms_phospho_peptide_conditions_contrasts[[contrast_idx]])[grep('adj_p',colnames(ms_phospho_peptide_conditions_contrasts[[contrast_idx]]))] = 'adj_pvalue'
}
names(ms_phospho_peptide_conditions_contrasts) = ms_comparisons
make_save_contrast_volcano_plots(ms_phospho_peptide_conditions_contrasts,dir=paste0(PLOTS_DIR,'MS_phospho_peptides/'),legend_title='reference level abundance')## Warning: Removed 27 rows containing missing values (geom_point).
## Removed 27 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 42 rows containing missing values (geom_point).## Warning: Removed 42 rows containing missing values (geom_point).
## Warning: Removed 56 rows containing missing values (geom_point).## Warning: Removed 56 rows containing missing values (geom_point).
Normalize phospho data
Normalize phospho-peptide comparison data Following Liang’s advice, normalize (subtract log2FC) phospho-peptide log2FC comparison data minus corresponding total protein protein-level comparison log2FC
ms_total_protein_conditions_contrasts_filtered = list()
for(i in 1:length(ms_total_protein_conditions_contrasts)){
ms_total_protein_conditions_contrasts_filtered[[i]] = ms_total_protein_conditions_contrasts[[i]][ms_total_protein_conditions_contrasts[[i]]$Protein == ms_total_protein_conditions_contrasts[[i]]$ProteinGroup,]
}
names(ms_total_protein_conditions_contrasts_filtered) = ms_comparisons
ms_phospho_peptide_norm_conditions_contrasts = ms_phospho_peptide_conditions_contrasts
merged_total_and_phospho_conditions_contrasts = ms_phospho_peptide_conditions_contrasts
for(contrast_idx in 1:length(ms_comparisons)){
curr_contrast = names(ms_phospho_peptide_norm_conditions_contrasts[contrast_idx])
merged_total_and_phospho_conditions_contrasts[[curr_contrast]] = merge(ms_phospho_peptide_conditions_contrasts[[curr_contrast]],ms_total_protein_conditions_contrasts_filtered[[curr_contrast]][,c('Protein','log2FC','adj_pvalue','numerator_mean','reference_mean')],by='Protein',all=FALSE,suffixes=c('_phospho','_total'))
merged_total_and_phospho_conditions_contrasts[[curr_contrast]]$log2FC_norm = merged_total_and_phospho_conditions_contrasts[[curr_contrast]]$log2FC_phospho - merged_total_and_phospho_conditions_contrasts[[curr_contrast]]$log2FC_total
}Gene-level RNA-seq
Read in count data
Using tximport (length scaled)
salmonAln_files = paste0(list.files(file.path (SALMON_DIR),pattern='RSA',full.names = TRUE),'/quant.sf')
names = list.files(file.path (SALMON_DIR),pattern='RSA',full.names = FALSE)
names(salmonAln_files) = names
salmonAln_files## RSA0000107803_RSA0000107683
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107803_RSA0000107683/quant.sf"
## RSA0000107804_RSA0000107684
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107804_RSA0000107684/quant.sf"
## RSA0000107805_RSA0000107685
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107805_RSA0000107685/quant.sf"
## RSA0000107806_RSA0000107686
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107806_RSA0000107686/quant.sf"
## RSA0000107807_RSA0000107687
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107807_RSA0000107687/quant.sf"
## RSA0000107808_RSA0000107688
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107808_RSA0000107688/quant.sf"
## RSA0000107809_RSA0000107689
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107809_RSA0000107689/quant.sf"
## RSA0000107810_RSA0000107690
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107810_RSA0000107690/quant.sf"
## RSA0000107811_RSA0000107691
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107811_RSA0000107691/quant.sf"
## RSA0000107812_RSA0000107692
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107812_RSA0000107692/quant.sf"
## RSA0000107813_RSA0000107693
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107813_RSA0000107693/quant.sf"
## RSA0000107814_RSA0000107694
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107814_RSA0000107694/quant.sf"
## RSA0000107815_RSA0000107695
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107815_RSA0000107695/quant.sf"
## RSA0000107816_RSA0000107696
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107816_RSA0000107696/quant.sf"
## RSA0000107817_RSA0000107697
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107817_RSA0000107697/quant.sf"
## RSA0000107818_RSA0000107698
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107818_RSA0000107698/quant.sf"
## RSA0000107819_RSA0000107699
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107819_RSA0000107699/quant.sf"
## RSA0000107820_RSA0000107700
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107820_RSA0000107700/quant.sf"
## RSA0000107821_RSA0000107701
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107821_RSA0000107701/quant.sf"
## RSA0000107822_RSA0000107702
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107822_RSA0000107702/quant.sf"
## RSA0000107823_RSA0000107703
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107823_RSA0000107703/quant.sf"
## RSA0000107824_RSA0000107704
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107824_RSA0000107704/quant.sf"
## RSA0000107825_RSA0000107705
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107825_RSA0000107705/quant.sf"
## RSA0000107826_RSA0000107706
## "Y:/DUX4_multiomics/DUX4_RNAseq/combined_caDUX4/results/star-salmon-t1xczk/salmon/RSA0000107826_RSA0000107706/quant.sf"salmon_gene_counts_lengthScaled <- tximport::tximport(salmonAln_files,type='salmon',tx2gene=biomart_tx2gene_caDUX4,countsFromAbundance='lengthScaledTPM')## reading in files with read_tsv## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## transcripts missing from tx2gene: 96
## summarizing abundance
## summarizing counts
## summarizing lengthsalmon_gene_counts_lengthScaled$counts %>%
round() %>%
as.data.frame() %>%
tibble::rownames_to_column('ensembl_id') %>%
tibble::as_tibble() %>%
dplyr::mutate_if(is.double, as.integer) %>%
readr::write_csv(file.path(RNASEQ_RESULTS_DIR,'salmon_gene_counts_lengthScaled.csv'))
#row.names(salmon_counts$counts) = lapply(row.names(salmon_counts$counts),truncate_gene_version)Filter and normalize
For limma
counts_lengthScaled = salmon_gene_counts_lengthScaled$counts
dge_lengthScaled = DGEList(counts=counts_lengthScaled)
dge_lengthScaled$samples$group = RNA_sample_metadata$condition
dim(dge_lengthScaled)## [1] 60624 24as.data.frame(apply(dge_lengthScaled$counts,2,median))## apply(dge_lengthScaled$counts, 2, median)
## RSA0000107803_RSA0000107683 0
## RSA0000107804_RSA0000107684 0
## RSA0000107805_RSA0000107685 0
## RSA0000107806_RSA0000107686 0
## RSA0000107807_RSA0000107687 0
## RSA0000107808_RSA0000107688 0
## RSA0000107809_RSA0000107689 0
## RSA0000107810_RSA0000107690 0
## RSA0000107811_RSA0000107691 0
## RSA0000107812_RSA0000107692 0
## RSA0000107813_RSA0000107693 0
## RSA0000107814_RSA0000107694 0
## RSA0000107815_RSA0000107695 0
## RSA0000107816_RSA0000107696 0
## RSA0000107817_RSA0000107697 0
## RSA0000107818_RSA0000107698 0
## RSA0000107819_RSA0000107699 0
## RSA0000107820_RSA0000107700 0
## RSA0000107821_RSA0000107701 0
## RSA0000107822_RSA0000107702 0
## RSA0000107823_RSA0000107703 0
## RSA0000107824_RSA0000107704 0
## RSA0000107825_RSA0000107705 0
## RSA0000107826_RSA0000107706 0# Filter low counts
min_counts = 2
min_samples = 4
idx_keep_genes = apply(counts_lengthScaled,1,function(x) sum(x>=min_counts)) >= min_samples
dge_lengthScaled_prefiltered = dge_lengthScaled[idx_keep_genes,]
dim(dge_lengthScaled_prefiltered)## [1] 22537 24as.data.frame(apply(dge_lengthScaled_prefiltered$counts,2,median))## apply(dge_lengthScaled_prefiltered$counts, 2, median)
## RSA0000107803_RSA0000107683 101.32017
## RSA0000107804_RSA0000107684 125.09342
## RSA0000107805_RSA0000107685 108.22994
## RSA0000107806_RSA0000107686 95.86566
## RSA0000107807_RSA0000107687 53.51786
## RSA0000107808_RSA0000107688 77.68297
## RSA0000107809_RSA0000107689 102.44394
## RSA0000107810_RSA0000107690 103.45741
## RSA0000107811_RSA0000107691 120.18044
## RSA0000107812_RSA0000107692 116.55706
## RSA0000107813_RSA0000107693 120.51982
## RSA0000107814_RSA0000107694 110.12530
## RSA0000107815_RSA0000107695 105.46270
## RSA0000107816_RSA0000107696 89.72790
## RSA0000107817_RSA0000107697 127.02133
## RSA0000107818_RSA0000107698 71.04003
## RSA0000107819_RSA0000107699 105.76433
## RSA0000107820_RSA0000107700 91.09496
## RSA0000107821_RSA0000107701 121.67501
## RSA0000107822_RSA0000107702 126.23000
## RSA0000107823_RSA0000107703 125.51527
## RSA0000107824_RSA0000107704 111.67002
## RSA0000107825_RSA0000107705 109.32710
## RSA0000107826_RSA0000107706 89.92093dge_lengthScaled_prefiltered_RLE = calcNormFactors(dge_lengthScaled_prefiltered,method='RLE')
cpm_lengthScaled_prefiltered_RLE = cpm(dge_lengthScaled_prefiltered_RLE)
cpm_condition_means = 0*cpm_lengthScaled_prefiltered_RLE[,1:length(unique(RNA_sample_metadata$condition))]
colnames(cpm_condition_means) = unique(RNA_sample_metadata$condition)
for(curr_condition in unique(RNA_sample_metadata$condition)){
cpm_condition_means[,curr_condition] = rowMeans(cpm_lengthScaled_prefiltered_RLE[,RNA_sample_metadata$sample_name[RNA_sample_metadata$condition==curr_condition]])
}
colnames(cpm_condition_means) = gsub('0WT','WT',colnames(cpm_condition_means))PCA
PCAres_edgeR_RLE_0 = prcomp(t(cpm_lengthScaled_prefiltered_RLE),center=TRUE,scale=TRUE)
PCAres_edgeR_RLE = merge(as.data.frame(PCAres_edgeR_RLE_0$x), RNA_sample_metadata,by.x=0,by.y=1)
percentVar_edgeR_RLE = round(100 * summary(PCAres_edgeR_RLE_0)$importance[2,])
ggplot(PCAres_edgeR_RLE,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('edgeR RLE norm CPM, centered and scaled') +
xlab(paste0("PC1: ",percentVar_edgeR_RLE[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_edgeR_RLE[2],"% variance"))
PCAres_edgeR_RLE_0 = prcomp(t(cpm_lengthScaled_prefiltered_RLE),center=TRUE,scale=FALSE)
PCAres_edgeR_RLE = merge(as.data.frame(PCAres_edgeR_RLE_0$x), RNA_sample_metadata,by.x=0,by.y=1)
percentVar_edgeR_RLE = round(100 * summary(PCAres_edgeR_RLE_0)$importance[2,])
ggplot(PCAres_edgeR_RLE,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('edgeR RLE norm CPM, centered, NOT scaled') +
xlab(paste0("PC1: ",percentVar_edgeR_RLE[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_edgeR_RLE[2],"% variance"))
prince_covars = mapping_metrics
prince_covars = prince_covars[, c('total_reads','multimapped_percent','PCT_CODING_BASES','PCT_MRNA_BASES','PCT_RIBOSOMAL_BASES','PCT_UTR_BASES','PCT_INTRONIC_BASES','PCT_INTERGENIC_BASES','percent_fails','percent_duplicates')]
row.names(prince_covars) = mapping_metrics$Sample
pca_corr = swamp::prince(cpm_lengthScaled_prefiltered_RLE, prince_covars, top=min(25,dim(cpm_lengthScaled_prefiltered_RLE)[2]))
# visualize significance of correlations
prince.plot(pca_corr)
prince.plot(pca_corr, Rsquared=TRUE)
mapping_metrics_labeled = merge(mapping_metrics, RNA_sample_metadata, by.x='Sample',by.y='sample_name')
ggplot(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],aes_string(x='condition', y='PCT_INTRONIC_BASES', color='condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
# stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
ylab('Percent intronic bases') +
ggtitle('Intronic Bases')
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/intronic_bases.pdf',device='pdf',width=10,height=8)
ggplot(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],aes_string(x='condition', y='PCT_INTRONIC_BASES', color='condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
ylab('Percent intronic bases') +
ggtitle('Intronic Bases')
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/intronic_bases_summary.pdf',device='pdf',width=10,height=8)
ggplot(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],aes_string(x='condition', y='PCT_INTERGENIC_BASES', color='condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
# stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
ylab('Percent intergenic bases') +
ggtitle('Intergenic Bases')
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/intergenic_bases.pdf',device='pdf',width=10,height=8)
ggplot(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],aes_string(x='condition', y='PCT_INTERGENIC_BASES', color='condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
ylab('Percent intergenic bases') +
ggtitle('Intergenic Bases')
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/intergenic_bases_summary.pdf',device='pdf',width=10,height=8)
#dev.off()
write.table(mapping_metrics_labeled[,c('condition','PCT_INTRONIC_BASES','PCT_INTERGENIC_BASES')],'Y:/DUX4_multiomics/DUX4_RNAseq/intronic_intergenic_pcts.txt',row.names=FALSE,sep='\t')
test_mapping_metric_statistics <- function(mapping_metrics_labeled,metric){
message(paste(metric))
anova_res = aov(get(metric) ~ condition,mapping_metrics_labeled[,c(metric,'condition')])
print(summary(anova_res))
write_lines(summary(anova_res),paste0('Y:/DUX4_multiomics/DUX4_RNAseq/',metric,'_anova.txt'))
print(TukeyHSD(anova_res))
write.table(TukeyHSD(anova_res)$condition,paste0('Y:/DUX4_multiomics/DUX4_RNAseq/',metric,'_TukeyHSD.txt'))
print(pairwise.t.test(mapping_metrics_labeled[,metric],mapping_metrics_labeled$condition,p.adjust.method='BH'))
write.table(pairwise.t.test(mapping_metrics_labeled[,metric],mapping_metrics_labeled$condition,p.adjust.method='BH')$p.value,paste0('Y:/DUX4_multiomics/DUX4_RNAseq/',metric,'_ttest_BHFDR.txt'))
}
test_mapping_metric_statistics(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],'PCT_INTRONIC_BASES')## PCT_INTRONIC_BASES## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 20.660 6.887 35.09 3.21e-06 ***
## Residuals 12 2.355 0.196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = get(metric) ~ condition, data = mapping_metrics_labeled[, c(metric, "condition")])
##
## $condition
## diff lwr upr p adj
## iDUX4t02dox-iDUX4dmso -0.042800 -0.9727778 0.8871778 0.9990363
## iDUX4t06dox-iDUX4dmso 1.306500 0.3765222 2.2364778 0.0061458
## iDUX4t14dox-iDUX4dmso 2.727325 1.7973472 3.6573028 0.0000081
## iDUX4t06dox-iDUX4t02dox 1.349300 0.4193222 2.2792778 0.0048592
## iDUX4t14dox-iDUX4t02dox 2.770125 1.8401472 3.7001028 0.0000069
## iDUX4t14dox-iDUX4t06dox 1.420825 0.4908472 2.3508028 0.0032938
##
##
## Pairwise comparisons using t tests with pooled SD
##
## data: mapping_metrics_labeled[, metric] and mapping_metrics_labeled$condition
##
## iDUX4dmso iDUX4t02dox iDUX4t06dox
## iDUX4t02dox 0.8936 - -
## iDUX4t06dox 0.0016 0.0015 -
## iDUX4t14dox 4.7e-06 4.7e-06 0.0014
##
## P value adjustment method: BHtest_mapping_metric_statistics(mapping_metrics_labeled[mapping_metrics_labeled$condition %in% conditions_to_plot,],'PCT_INTERGENIC_BASES')## PCT_INTERGENIC_BASES## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 9.561 3.187 215.7 1.06e-10 ***
## Residuals 12 0.177 0.015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = get(metric) ~ condition, data = mapping_metrics_labeled[, c(metric, "condition")])
##
## $condition
## diff lwr upr p adj
## iDUX4t02dox-iDUX4dmso 0.12305 -0.13213361 0.3782336 0.5048435
## iDUX4t06dox-iDUX4dmso 0.44060 0.18541639 0.6957836 0.0012368
## iDUX4t14dox-iDUX4dmso 1.93405 1.67886639 2.1892336 0.0000000
## iDUX4t06dox-iDUX4t02dox 0.31755 0.06236639 0.5727336 0.0140775
## iDUX4t14dox-iDUX4t02dox 1.81100 1.55581639 2.0661836 0.0000000
## iDUX4t14dox-iDUX4t06dox 1.49345 1.23826639 1.7486336 0.0000000
##
##
## Pairwise comparisons using t tests with pooled SD
##
## data: mapping_metrics_labeled[, metric] and mapping_metrics_labeled$condition
##
## iDUX4dmso iDUX4t02dox iDUX4t06dox
## iDUX4t02dox 0.17778 - -
## iDUX4t06dox 0.00038 0.00368 -
## iDUX4t14dox 2.1e-10 2.3e-10 1.4e-09
##
## P value adjustment method: BHintronic_anova_res = aov(PCT_INTRONIC_BASES ~ condition,mapping_metrics_labeled[,c('PCT_INTRONIC_BASES','condition')])
summary(intronic_anova_res)## Df Sum Sq Mean Sq F value Pr(>F)
## condition 5 21.309 4.262 26.61 1.01e-07 ***
## Residuals 18 2.883 0.160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1write_lines(summary(intronic_anova_res),'Y:/DUX4_multiomics/DUX4_RNAseq/intronic_anova.txt')
TukeyHSD(intronic_anova_res)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PCT_INTRONIC_BASES ~ condition, data = mapping_metrics_labeled[, c("PCT_INTRONIC_BASES", "condition")])
##
## $condition
## diff lwr upr p adj
## 0WTt14dox-0WTdmso 0.180150 -0.7192491 1.07954905 0.9865495
## iDUX4dmso-0WTdmso -0.576725 -1.4761241 0.32267405 0.3605610
## iDUX4t02dox-0WTdmso -0.619525 -1.5189241 0.27987405 0.2896929
## iDUX4t06dox-0WTdmso 0.729775 -0.1696241 1.62917405 0.1535306
## iDUX4t14dox-0WTdmso 2.150600 1.2512009 3.04999905 0.0000066
## iDUX4dmso-0WTt14dox -0.756875 -1.6562741 0.14252405 0.1296290
## iDUX4t02dox-0WTt14dox -0.799675 -1.6990741 0.09972405 0.0983701
## iDUX4t06dox-0WTt14dox 0.549625 -0.3497741 1.44902405 0.4102945
## iDUX4t14dox-0WTt14dox 1.970450 1.0710509 2.86984905 0.0000216
## iDUX4t02dox-iDUX4dmso -0.042800 -0.9421991 0.85659905 0.9999868
## iDUX4t06dox-iDUX4dmso 1.306500 0.4071009 2.20589905 0.0025009
## iDUX4t14dox-iDUX4dmso 2.727325 1.8279259 3.62672405 0.0000002
## iDUX4t06dox-iDUX4t02dox 1.349300 0.4499009 2.24869905 0.0018160
## iDUX4t14dox-iDUX4t02dox 2.770125 1.8707259 3.66952405 0.0000002
## iDUX4t14dox-iDUX4t06dox 1.420825 0.5214259 2.32022405 0.0010661write.table(TukeyHSD(intronic_anova_res)$condition,'Y:/DUX4_multiomics/DUX4_RNAseq/intronic_TukeyHSD.txt')
pairwise.t.test(mapping_metrics_labeled$PCT_INTRONIC_BASES,mapping_metrics_labeled$condition,p.adjust.method='BH')##
## Pairwise comparisons using t tests with pooled SD
##
## data: mapping_metrics_labeled$PCT_INTRONIC_BASES and mapping_metrics_labeled$condition
##
## 0WTdmso 0WTt14dox iDUX4dmso iDUX4t02dox iDUX4t06dox
## 0WTt14dox 0.57045 - - - -
## iDUX4dmso 0.07067 0.02578 - - -
## iDUX4t02dox 0.05729 0.02101 0.88147 - -
## iDUX4t06dox 0.02839 0.07839 0.00046 0.00038 -
## iDUX4t14dox 2.5e-06 6.3e-06 1.2e-07 1.2e-07 0.00027
##
## P value adjustment method: BHwrite.table(pairwise.t.test(mapping_metrics_labeled$PCT_INTRONIC_BASES,mapping_metrics_labeled$condition,p.adjust.method='BH')$p.value,'Y:/DUX4_multiomics/DUX4_RNAseq/intronic_ttest_BHFDR.txt')Contrasts
design_condition = model.matrix(~0+condition,RNA_sample_metadata)
design_condition## condition0WTdmso condition0WTt14dox conditioniDUX4dmso conditioniDUX4t02dox
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 1 0 0 0
## 10 1 0 0 0
## 11 1 0 0 0
## 12 1 0 0 0
## 13 0 0 0 1
## 14 0 0 0 1
## 15 0 0 0 1
## 16 0 0 0 1
## 17 0 1 0 0
## 18 0 1 0 0
## 19 0 1 0 0
## 20 0 1 0 0
## 21 0 0 1 0
## 22 0 0 1 0
## 23 0 0 1 0
## 24 0 0 1 0
## conditioniDUX4t06dox conditioniDUX4t14dox
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 0 1
## 6 0 1
## 7 0 1
## 8 0 1
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## attr(,"assign")
## [1] 1 1 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"v_lengthScaled_prefiltered_all = voom(dge_lengthScaled_prefiltered_RLE,design_condition,plot=TRUE)
PCAres_voom_0 = prcomp(t(cpm_lengthScaled_prefiltered_RLE),center=TRUE,scale=TRUE)
PCAres_voom = merge(as.data.frame(PCAres_voom_0$x), RNA_sample_metadata,by.x=0,by.y=1)
percentVar_PCAres_voom = round(100 * summary(PCAres_voom_0)$importance[2,])
ggplot(PCAres_voom,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('voom transformed, centered and scaled') +
xlab(paste0("PC1: ",percentVar_PCAres_voom[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_PCAres_voom[2],"% variance"))
#colnames(model_contrasts_of_interest) = colnames(design_condition)
rownames(model_contrasts_of_interest) = contrasts_of_interest$Contrast
fit_lengthScaled_prefiltered_all = lmFit(v_lengthScaled_prefiltered_all,design_condition)
fit_lengthScaled_prefiltered_all_contrasts = contrasts.fit(fit_lengthScaled_prefiltered_all,t(model_contrasts_of_interest))
fit_lengthScaled_prefiltered_all_eBayes = eBayes(fit_lengthScaled_prefiltered_all_contrasts,proportion=0.5)Contrast volcano plots
limma_res_lengthScaled_conditions_contrasts = list()
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
curr_contrast = rownames(model_contrasts_of_interest)[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
curr_condition_means = cpm_condition_means[,c(curr_numerator,curr_reference)]
colnames(curr_condition_means) = c('numerator_mean','reference_mean')
limma_res_lengthScaled_conditions_contrasts[[contrast_idx]] = merge(topTable(fit_lengthScaled_prefiltered_all_eBayes,coef=rownames(model_contrasts_of_interest)[contrast_idx],number=1e7),curr_condition_means,by.x=0,by.y=0,all.x=TRUE,all.y=FALSE)
limma_res_lengthScaled_conditions_contrasts[[contrast_idx]] = merge(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]],biomartGeneAnnot,by.x='Row.names',by.y=1,all.x=TRUE,all.y=FALSE)
colnames(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]])[grep('logFC',colnames(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]]))] = 'log2FC'
colnames(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]])[grep('adj',colnames(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]]))] = 'adj_pvalue'
}
names(limma_res_lengthScaled_conditions_contrasts) = rownames(model_contrasts_of_interest)
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
curr_contrast = rownames(model_contrasts_of_interest)[contrast_idx]
write.table(limma_res_lengthScaled_conditions_contrasts,paste0(RNASEQ_ANALYSIS_DIR,'/',curr_contrast,'_limma_res_lengthScaled.txt'),sep='\t',row.names=FALSE)
}
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
curr_contrast = rownames(model_contrasts_of_interest)[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
log2FC_lim = max(max(unlist(lapply(limma_res_lengthScaled_conditions_contrasts,function(x) max(x$log2FC,na.rm=TRUE)))),-1*min(unlist(lapply(limma_res_lengthScaled_conditions_contrasts,function(x) min(x$log2FC,na.rm=TRUE)))))
max_neg_log_adj_p = max(unlist(lapply(limma_res_lengthScaled_conditions_contrasts,function(x) max(-log10(x$adj_pvalue),na.rm=TRUE))))
print(ggplot(limma_res_lengthScaled_conditions_contrasts[[curr_contrast]],aes(x=log2FC,y=-log10(adj_pvalue),color=(reference_mean>1))) + geom_point(alpha=0.5,shape=16) + ggtitle(paste('condition contrasts model:',curr_contrast)) + xlab('log2(fold change)') + ylab('-log10(adjusted p-value)') + scale_color_manual(values = c("#008837","#7b3294"),name='reference mean CPM > 1') + theme_bw() + coord_cartesian(xlim=log2FC_lim*c(-1.05,1.05),ylim=c(0,1.05*max_neg_log_adj_p)))
ggsave(paste('Y:/DUX4_multiomics/Analysis/volcano_plots_lessthan1/',curr_contrast,'.pdf',sep=''),device='pdf',width=10,height=8)
}
make_save_contrast_volcano_plots(limma_res_lengthScaled_conditions_contrasts,dir=paste0(PLOTS_DIR,'RNAseq_gene_level/'),color_by='reference_mean',color_trans='log10')## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
Plot selected genes
cpm_lengthScaled_prefiltered_RLE_to_plot = as.data.frame(t(cpm_lengthScaled_prefiltered_RLE))
all(row.names(cpm_lengthScaled_prefiltered_RLE_to_plot)==RNA_sample_metadata$sample_name)## [1] TRUEcpm_lengthScaled_prefiltered_RLE_to_plot$Sample = RNA_sample_metadata$sample_name
cpm_lengthScaled_prefiltered_RLE_to_plot$Condition = RNA_sample_metadata$condition
cpm_lengthScaled_prefiltered_RLE_to_plot$Cells = RNA_sample_metadata$cells
cpm_lengthScaled_prefiltered_RLE_to_plot$Timepoint = RNA_sample_metadata$treatment
ggplot(cpm_lengthScaled_prefiltered_RLE_to_plot,aes_string(x='Condition',y='caDUX4_sequence',color='Condition')) + geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
labs( title = 'caDUX4' ) +
ylab('caDUX4 length scaled normalized CPM')
plot_gene_normalized_counts <- function(curr_gene_symbol, cpm_to_plot){
curr_gene_id = biomartGeneAnnot$ensembl_gene_id[biomartGeneAnnot$external_gene_name==curr_gene_symbol]
if(is.na(curr_gene_id)){
message('gene id not found')
} else {
ggplot(cpm_to_plot,aes_string(x='Condition', y=curr_gene_id, color='Condition')) +
geom_jitter(width=0.1,height=0,size=2.5) +
theme(axis.text.x = element_text(angle = 90)) +
stat_summary( fun.data = 'mean_se', geom = 'crossbar' ) +
labs( title = paste(curr_gene_symbol,' (',curr_gene_id,')',sep='')) +
ylab(paste(curr_gene_id,' length scaled normalized CPM'))
}
}
plot_gene_normalized_counts('MBD3L2',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('ZSCAN4',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('LEUTX',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('DUXA',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('TRIM43',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('PRAMEF12',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('MYOD1',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('PAX7',cpm_lengthScaled_prefiltered_RLE_to_plot)
plot_gene_normalized_counts('SMCHD1',cpm_lengthScaled_prefiltered_RLE_to_plot)
# Plot selected conditions only
cpm_lengthScaled_prefiltered_RLE_to_plot_selected_conditions = cpm_lengthScaled_prefiltered_RLE_to_plot[cpm_lengthScaled_prefiltered_RLE_to_plot$Condition %in% c('iDUX4dmso','iDUX4t02dox','iDUX4t06dox','iDUX4t14dox'),]
plot_gene_normalized_counts('ZSCAN4',cpm_lengthScaled_prefiltered_RLE_to_plot_selected_conditions)
ggsave(paste('Y:/DUX4_multiomics/Analysis/RNAseq_gene_plots/ZSCAN4.pdf',sep=''),device='pdf',width=10,height=8)
plot_gene_normalized_counts('MBD3L2',cpm_lengthScaled_prefiltered_RLE_to_plot_selected_conditions)
ggsave(paste('Y:/DUX4_multiomics/Analysis/RNAseq_gene_plots/MBD3L2.pdf',sep=''),device='pdf',width=10,height=8)
plot_gene_normalized_counts('LEUTX',cpm_lengthScaled_prefiltered_RLE_to_plot_selected_conditions)
ggsave(paste('Y:/DUX4_multiomics/Analysis/RNAseq_gene_plots/LEUTX.pdf',sep=''),device='pdf',width=10,height=8)
write.table(cpm_lengthScaled_prefiltered_RLE_to_plot[,c(colnames(cpm_lengthScaled_prefiltered_RLE_to_plot)[which(colnames(cpm_lengthScaled_prefiltered_RLE_to_plot) %in% biomartGeneAnnot[biomartGeneAnnot$external_gene_name %in% c('ZSCAN4','MBD3L2','LEUTX','caDUX4'),'ensembl_gene_id'])],'Condition')],'Y:/DUX4_multiomics/Analysis/RNAseq_gene_plots/lengthScaled_normalized_CPM_selected.txt',sep='\t',row.names=TRUE)Transcript-level analysis
Read depth is low for transcriptome-level assignment of transcripts. Results will be low-confidence but it may be possible to gain some information (in combination with LSV from MAJIQ) on the functionality of differentially expressed isoforms. I will try it, but all results should be viewed with caution and we should evaluate whether (inferred) differentially expressed isoforms identified here are consistent with MAJIQ results and actual reads before experimental follow-up.
Read in data
Transcript-level count data with tx import (length scaled)
Filter and normalize
transcript_counts_lengthScaled = salmon_transcript_counts_lengthScaled$counts
transcript_dge_lengthScaled = DGEList(counts=transcript_counts_lengthScaled)
transcript_dge_lengthScaled$samples$group = RNA_sample_metadata$condition
dim(transcript_dge_lengthScaled)## [1] 227465 24as.data.frame(apply(transcript_dge_lengthScaled$counts,2,median))## apply(transcript_dge_lengthScaled$counts, 2, median)
## RSA0000107803_RSA0000107683 0
## RSA0000107804_RSA0000107684 0
## RSA0000107805_RSA0000107685 0
## RSA0000107806_RSA0000107686 0
## RSA0000107807_RSA0000107687 0
## RSA0000107808_RSA0000107688 0
## RSA0000107809_RSA0000107689 0
## RSA0000107810_RSA0000107690 0
## RSA0000107811_RSA0000107691 0
## RSA0000107812_RSA0000107692 0
## RSA0000107813_RSA0000107693 0
## RSA0000107814_RSA0000107694 0
## RSA0000107815_RSA0000107695 0
## RSA0000107816_RSA0000107696 0
## RSA0000107817_RSA0000107697 0
## RSA0000107818_RSA0000107698 0
## RSA0000107819_RSA0000107699 0
## RSA0000107820_RSA0000107700 0
## RSA0000107821_RSA0000107701 0
## RSA0000107822_RSA0000107702 0
## RSA0000107823_RSA0000107703 0
## RSA0000107824_RSA0000107704 0
## RSA0000107825_RSA0000107705 0
## RSA0000107826_RSA0000107706 0# Filter low counts
transcript_idx_keep_genes = apply(transcript_counts_lengthScaled,1,function(x) sum(x>=min_counts)) >= min_samples
transcript_dge_lengthScaled_prefiltered = transcript_dge_lengthScaled[transcript_idx_keep_genes,]
dim(transcript_dge_lengthScaled_prefiltered)## [1] 98583 24as.data.frame(apply(transcript_dge_lengthScaled_prefiltered$counts,2,median))## apply(transcript_dge_lengthScaled_prefiltered$counts, 2, median)
## RSA0000107803_RSA0000107683 9.186366
## RSA0000107804_RSA0000107684 11.492367
## RSA0000107805_RSA0000107685 10.083535
## RSA0000107806_RSA0000107686 8.192418
## RSA0000107807_RSA0000107687 5.536859
## RSA0000107808_RSA0000107688 8.260102
## RSA0000107809_RSA0000107689 11.399260
## RSA0000107810_RSA0000107690 11.214004
## RSA0000107811_RSA0000107691 11.195994
## RSA0000107812_RSA0000107692 11.188850
## RSA0000107813_RSA0000107693 11.402396
## RSA0000107814_RSA0000107694 10.070158
## RSA0000107815_RSA0000107695 9.308705
## RSA0000107816_RSA0000107696 6.856725
## RSA0000107817_RSA0000107697 10.970384
## RSA0000107818_RSA0000107698 5.362795
## RSA0000107819_RSA0000107699 9.821525
## RSA0000107820_RSA0000107700 7.860204
## RSA0000107821_RSA0000107701 11.530450
## RSA0000107822_RSA0000107702 11.803650
## RSA0000107823_RSA0000107703 11.344548
## RSA0000107824_RSA0000107704 9.120544
## RSA0000107825_RSA0000107705 10.312965
## RSA0000107826_RSA0000107706 8.100109transcript_dge_lengthScaled_prefiltered_RLE = calcNormFactors(transcript_dge_lengthScaled_prefiltered,method='RLE')
transcript_cpm_lengthScaled_prefiltered_RLE = cpm(transcript_dge_lengthScaled_prefiltered_RLE)
transcript_cpm_condition_means = 0*transcript_cpm_lengthScaled_prefiltered_RLE[,1:length(unique(RNA_sample_metadata$condition))]
colnames(transcript_cpm_condition_means) = unique(RNA_sample_metadata$condition)
for(curr_condition in unique(RNA_sample_metadata$condition)){
transcript_cpm_condition_means[,curr_condition] = rowMeans(transcript_cpm_lengthScaled_prefiltered_RLE[,RNA_sample_metadata$sample_name[RNA_sample_metadata$condition==curr_condition]])
}
colnames(transcript_cpm_condition_means) = gsub('0WT','WT',colnames(transcript_cpm_condition_means))
transcript_cpm_condition_means_ann = merge(transcript_cpm_condition_means, biomartTranscriptAnnot,by.x=0,by.y='ensembl_transcript_id_version',all.x=TRUE,all.y=FALSE)PCA
Exploratory data analysis: PCA on transcript-level data
PCAres_edgeR_RLE_transcript_0 = prcomp(t(transcript_cpm_lengthScaled_prefiltered_RLE),center=TRUE,scale=TRUE)
PCAres_edgeR_RLE_transcript = merge(as.data.frame(PCAres_edgeR_RLE_transcript_0$x), RNA_sample_metadata,by.x=0,by.y=1)
percentVar_edgeR_RLE_transcript = round(100 * summary(PCAres_edgeR_RLE_transcript_0)$importance[2,])
ggplot(PCAres_edgeR_RLE_transcript,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('edgeR RLE norm transcript CPM, centered and scaled') +
xlab(paste0("PC1: ",percentVar_edgeR_RLE[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_edgeR_RLE[2],"% variance"))
PCAres_edgeR_RLE_transcript_0 = prcomp(t(transcript_cpm_lengthScaled_prefiltered_RLE),center=TRUE,scale=FALSE)
PCAres_edgeR_RLE_transcript = merge(as.data.frame(PCAres_edgeR_RLE_transcript_0$x), RNA_sample_metadata,by.x=0,by.y=1)
percentVar_edgeR_RLE_transcript = round(100 * summary(PCAres_edgeR_RLE_transcript_0)$importance[2,])
ggplot(PCAres_edgeR_RLE_transcript,aes(PC1,PC2,color=condition)) + geom_point() +
ggtitle('edgeR RLE norm transcript CPM, centered, NOT scaled') +
xlab(paste0("PC1: ",percentVar_edgeR_RLE[1],"% variance")) +
ylab(paste0("PC2: ",percentVar_edgeR_RLE[2],"% variance"))
prince_covars = mapping_metrics
prince_covars = prince_covars[, c('total_reads','multimapped_percent','PCT_CODING_BASES','PCT_MRNA_BASES','PCT_RIBOSOMAL_BASES','PCT_UTR_BASES','PCT_INTRONIC_BASES','PCT_INTERGENIC_BASES','percent_fails','percent_duplicates')]
row.names(prince_covars) = mapping_metrics$Sample
pca_corr = swamp::prince(transcript_cpm_lengthScaled_prefiltered_RLE, prince_covars, top=min(25,dim(transcript_cpm_lengthScaled_prefiltered_RLE)[2]))
# visualize significance of correlations
prince.plot(pca_corr)
prince.plot(pca_corr, Rsquared=TRUE)
Voom all transcript-level data
v_lengthScaled_prefiltered_transcript_all = voom(transcript_dge_lengthScaled_prefiltered_RLE,design_condition,plot=TRUE)
fit_lengthScaled_prefiltered_transcript_all = lmFit(v_lengthScaled_prefiltered_transcript_all,design_condition)
fit_lengthScaled_prefiltered_transcript_all_contrasts = contrasts.fit(fit_lengthScaled_prefiltered_transcript_all,t(model_contrasts_of_interest))
fit_lengthScaled_prefiltered_transcript_all_eBayes = eBayes(fit_lengthScaled_prefiltered_transcript_all_contrasts,proportion=0.5)Transcript-level contrasts
transcript_limma_res_lengthScaled_conditions_contrasts = list()
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
message(paste(rownames(model_contrasts_of_interest)[[contrast_idx]]))
curr_contrast = rownames(model_contrasts_of_interest)[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][1]
curr_condition_means = transcript_cpm_condition_means[,c(curr_numerator,curr_reference)]
colnames(curr_condition_means) = c('numerator_mean','reference_mean')
transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]] = merge(topTable(fit_lengthScaled_prefiltered_transcript_all_eBayes,coef=rownames(model_contrasts_of_interest)[contrast_idx],number=1e7),curr_condition_means,by.x=0,by.y=0,all.x=TRUE,all.y=FALSE)
transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]] = merge(transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]],biomartTranscriptAnnot,by.x='Row.names',by.y='ensembl_transcript_id_version',all.x=TRUE,all.y=FALSE)
colnames(transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]])[grep('logFC',colnames(transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]]))] = 'log2FC'
colnames(transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]])[grep('adj',colnames(transcript_limma_res_lengthScaled_conditions_contrasts[[contrast_idx]]))] = 'adj_pvalue'
}## iDUX4dmso_WTdmso## iDUX4t14dox_WTt14dox## iDUX4t02dox_iDUX4dmso## iDUX4t06dox_iDUX4dmso## iDUX4t14dox_iDUX4dmso## WTt14dox_WTdmsonames(transcript_limma_res_lengthScaled_conditions_contrasts) = rownames(model_contrasts_of_interest)Transcript volcano plots
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
curr_contrast = rownames(model_contrasts_of_interest)[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
print(ggplot(transcript_limma_res_lengthScaled_conditions_contrasts[[curr_contrast]],aes(x=log2FC,y=-log10(adj_pvalue),color=(reference_mean>1))) + geom_point(alpha=0.5,shape=16) + ggtitle(paste('condition contrasts model:',curr_contrast)) + xlab('log2(fold change)') + ylab('-log10(adjusted p-value)') + scale_color_manual(values = c("#008837","#7b3294"),name='reference mean CPM > 1') + theme_bw())
ggsave(paste('Y:/DUX4_multiomics/Analysis/volcano_plots_lessthan1/transcripts_',curr_contrast,'.pdf',sep=''),device='pdf',width=10,height=8)
}
make_save_contrast_volcano_plots(transcript_limma_res_lengthScaled_conditions_contrasts,dir=paste0(PLOTS_DIR,'RNAseq_transcript_level/'),color_by='reference_mean',color_trans='log10')## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Transformation introduced infinite values in discrete y-axis
hist(transcript_limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_WTt14dox$log2FC[transcript_limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_WTt14dox$transcript_biotype=='protein_coding'],c(-180:180)/10,main='protein coding transcripts')
hist(transcript_limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_WTt14dox$log2FC[transcript_limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_WTt14dox$transcript_biotype=='nonsense_mediated_decay'],c(-180:180)/10,main='nonsense mediated decay transcripts')
fGSEA
Gene level
Run fGSEA on RNA-seq gene-level data, using REACTOME gene sets
# run_gsea from setup section
gsea_nreps = 1e7
reactome_res <- httr::GET('http://csei.pfizer.com:8132/v4/dataset/GDMJT/export/ensembl/tsv?label=C2:CP:REACTOME')
gsi_pathways_reactome = readr::read_delim(content(reactome_res, as='text'), delim='\t', comment='#')## Rows: 1453 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): accession, name, systematic_name, items
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.reactome_pathways = lapply(gsi_pathways_reactome$items,function(x) strsplit(x,',')[[1]])
names(reactome_pathways) = gsi_pathways_reactome$name
hallmark_sets <- httr::GET('http://csei.pfizer.com:8132/v4/dataset/GDMJT/export/ensembl/tsv?label=H')
gsi_pathways_hallmark = readr::read_delim(content(hallmark_sets, as='text'), delim='\t', comment='#')## Rows: 50 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): accession, name, systematic_name, items
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.hallmark_pathways = lapply(gsi_pathways_hallmark$items,function(x) strsplit(x,',')[[1]])
names(hallmark_pathways) = gsi_pathways_hallmark$name
limma_res_lengthScaled_conditions_contrasts_forGsea = list()
fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome = list()
fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark = list()
for(contrast_idx in 1:length(limma_res_lengthScaled_conditions_contrasts)){
curr_contrast = names(limma_res_lengthScaled_conditions_contrasts)[contrast_idx]
limma_res_lengthScaled_conditions_contrasts_forGsea[[curr_contrast]] = limma_res_lengthScaled_conditions_contrasts[[curr_contrast]]$log2FC
names(limma_res_lengthScaled_conditions_contrasts_forGsea[[curr_contrast]]) = limma_res_lengthScaled_conditions_contrasts[[curr_contrast]]$Row.names
if(run_gsea){
fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]] = fgsea(reactome_pathways, limma_res_lengthScaled_conditions_contrasts_forGsea[[curr_contrast]],nperm = gsea_nreps, minSize = 1, maxSize = 2000,gseaParam = 1,nproc=1)
fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]] = fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]][order(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]]$pval),]
fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]][1:20,1:7]
Write.table(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]][,1:7],paste(GSEA_DIR,'/RNAseq_geneLevel/RNA_',curr_contrast,'_reactome.txt',sep=''),sep='\t',row.names=FALSE)
fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]] = fgsea(hallmark_pathways, limma_res_lengthScaled_conditions_contrasts_forGsea[[curr_contrast]],nperm = gsea_nreps, minSize = 1, maxSize = 2000,gseaParam = 1,nproc=1)
fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]] = fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]][order(fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]]$pval),]
fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]][1:20,1:7]
write.table(fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]][,1:7],paste(GSEA_DIR,'/RNAseq_geneLevel/RNA_',curr_contrast,'_hallmark.txt',sep=''),sep='\t',row.names=FALSE)
} else {
curr_contrast = names(limma_res_lengthScaled_conditions_contrasts)[contrast_idx]
fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[curr_contrast]] = read.table(paste(GSEA_DIR,'/RNAseq_geneLevel/RNA_',curr_contrast,'_reactome.txt',sep=''),sep='\t',header=TRUE)
fgsea_RNAseq_lengthScaled_conditions_contrasts_hallmark[[curr_contrast]] = read.table(paste(GSEA_DIR,'/RNAseq_geneLevel/RNA_',curr_contrast,'_hallmark.txt',sep=''),sep='\t',header=TRUE)
message(paste('warning: fgsea was not run, results read from file'))
}
}## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from filefgsea_RNAseq_lengthScaled_reactome_table_1 = merge(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[1]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[2]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome)[1:2],collapse=NULL,sep=''))
fgsea_RNAseq_lengthScaled_reactome_table_2 = merge(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[3]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[4]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome)[3:4],collapse=NULL,sep=''))
fgsea_RNAseq_lengthScaled_reactome_table_3 = merge(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[5]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[6]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome)[5:6],collapse=NULL,sep=''))
fgsea_RNAseq_lengthScaled_reactome_table = merge(fgsea_RNAseq_lengthScaled_reactome_table_1,fgsea_RNAseq_lengthScaled_reactome_table_2,by='pathway',all=TRUE)
fgsea_RNAseq_lengthScaled_reactome_table = merge(fgsea_RNAseq_lengthScaled_reactome_table,fgsea_RNAseq_lengthScaled_reactome_table_3,by='pathway',all=TRUE)
fgsea_RNAseq_lengthScaled_reactome_table_1 = NULL
fgsea_RNAseq_lengthScaled_reactome_table_2 = NULL
fgsea_RNAseq_lengthScaled_reactome_table_3 = NULL
fgsea_RNAseq_lengthScaled_reactome_table = fgsea_RNAseq_lengthScaled_reactome_table[order(apply(fgsea_RNAseq_lengthScaled_reactome_table[,grep('padj',colnames(fgsea_RNAseq_lengthScaled_reactome_table))],1,min)),]Enrichment plots
aheatmap(as.matrix(fgsea_RNAseq_lengthScaled_reactome_table[,grep('NES',colnames(fgsea_RNAseq_lengthScaled_reactome_table))]))
cpm_lengthScaled_prefiltered_RLE_to_plot = cpm_lengthScaled_prefiltered_RLE_to_plot[order(cpm_lengthScaled_prefiltered_RLE_to_plot$Condition),]
# compare pseudo-volcano plots in "real" vs "WT only" comparisons
plot(fgsea_RNAseq_lengthScaled_reactome_table$NES_iDUX4t14dox_WTt14dox,-log10(fgsea_RNAseq_lengthScaled_reactome_table$padj_iDUX4t14dox_WTt14dox),col='red',pch=19)
points(fgsea_RNAseq_lengthScaled_reactome_table$NES_iDUX4dmso_WTdmso,-log10(fgsea_RNAseq_lengthScaled_reactome_table$padj_iDUX4dmso_WTdmso),col='blue',pch=19)
points(fgsea_RNAseq_lengthScaled_reactome_table$NES_WTt14dox_WTdmso,-log10(fgsea_RNAseq_lengthScaled_reactome_table$padj_WTt14dox_WTdmso),col='black',pch=19)
make_pathway_plots <- function(curr_pathway,category){
dir.create(paste0(GSEA_DIR,'/RNAseq_geneLevel/',category,'/'))
dir.create(paste0(GSEA_DIR,'/RNAseq_geneLevel/',category,'/',curr_pathway))
# curr_pathway = 'REACTOME_OXIDATIVE_STRESS_INDUCED_SENESCENCE'
curr_pathway_ESmax = max(abs(fgsea_RNAseq_lengthScaled_reactome_table[fgsea_RNAseq_lengthScaled_reactome_table$pathway==curr_pathway,grep('ES',colnames(fgsea_RNAseq_lengthScaled_reactome_table))[c(1:6)*2-1]]))
barplot(t(fgsea_RNAseq_lengthScaled_reactome_table[fgsea_RNAseq_lengthScaled_reactome_table$pathway==curr_pathway,grep('NES',colnames(fgsea_RNAseq_lengthScaled_reactome_table))]),beside=TRUE,names.arg=names(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome),las=3,main=curr_pathway)
curr_pathway_barplot_data = as.data.frame(t(fgsea_RNAseq_lengthScaled_reactome_table[fgsea_RNAseq_lengthScaled_reactome_table$pathway==curr_pathway,grep('NES',colnames(fgsea_RNAseq_lengthScaled_reactome_table))]))
colnames(curr_pathway_barplot_data) = paste(curr_pathway)
curr_pathway_barplot_data$comparison = row.names(curr_pathway_barplot_data)
curr_pathway_barplot_data$comparison = gsub('NES_','',curr_pathway_barplot_data$comparison)
curr_pathway_barplot_data$NES = curr_pathway_barplot_data[,curr_pathway]
ggplot(curr_pathway_barplot_data,aes(comparison,NES)) + geom_col()
ggsave(paste(GSEA_DIR,'/RNAseq_geneLevel/',category,'/',curr_pathway,'/NES_barplot.pdf',sep=''),device='pdf',width=10,height=14)
ep1 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[1]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[1]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[1]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[1]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep2 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[2]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[2]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[2]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[2]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep3 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[3]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[3]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[3]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[3]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep4 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[4]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[4]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[4]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[4]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep5 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[5]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[5]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[5]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[5]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep6 = plotEnrichment(reactome_pathways[[curr_pathway]],limma_res_lengthScaled_conditions_contrasts_forGsea[[6]]) + ggtitle(paste(names(limma_res_lengthScaled_conditions_contrasts_forGsea)[[6]],'p_adj',format(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[6]]$padj[fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome[[6]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
#grid.arrange(ep1,ep3,ep2,ep4,ep6,ep5,ncol=2)
ep_all_curr_pathway = grid.arrange(ep1,ep3,ep2,ep4,ep6,ep5,ncol=2)
ggsave(paste(GSEA_DIR,'/RNAseq_geneLevel/',category,'/',curr_pathway,'/enrichment_plot.pdf',sep=''),plot=ep_all_curr_pathway,device='pdf',width=10,height=14)
curr_pathway_genes = reactome_pathways[[curr_pathway]]
ph=pheatmap(t(log10(cpm_lengthScaled_prefiltered_RLE_to_plot[,colnames(cpm_lengthScaled_prefiltered_RLE_to_plot) %in% curr_pathway_genes]+1)),labels_col=cpm_lengthScaled_prefiltered_RLE_to_plot$Condition,labels_row=biomartGeneAnnot$external_gene_name[match(as.character(colnames(cpm_lengthScaled_prefiltered_RLE_to_plot)[colnames(cpm_lengthScaled_prefiltered_RLE_to_plot) %in% curr_pathway_genes]),as.character(biomartGeneAnnot$ensembl_gene_id))],cluster_cols=FALSE)
ggsave(paste(GSEA_DIR,'/RNAseq_geneLevel/',category,'/',curr_pathway,'/genes_heatmap.pdf',sep=''),plot=ph,device='pdf',width=10,height=14)
ph_scaled=pheatmap(t(log10(cpm_lengthScaled_prefiltered_RLE_to_plot[,colnames(cpm_lengthScaled_prefiltered_RLE_to_plot) %in% curr_pathway_genes]+1)),labels_col=cpm_lengthScaled_prefiltered_RLE_to_plot$Condition,labels_row=biomartGeneAnnot$external_gene_name[match(as.character(colnames(cpm_lengthScaled_prefiltered_RLE_to_plot)[colnames(cpm_lengthScaled_prefiltered_RLE_to_plot) %in% curr_pathway_genes]),as.character(biomartGeneAnnot$ensembl_gene_id))],cluster_cols=FALSE,scale='row')
ggsave(paste(GSEA_DIR,'/RNAseq_geneLevel/',category,'/',curr_pathway,'/genes_heatmap_scaled.pdf',sep=''),plot=ph_scaled,device='pdf',width=10,height=14)
}
for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[1:6]){make_pathway_plots(curr_pathway,category='significant')}## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_CONDENSATION_OF_PROPHASE_CHROMOSOMES'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_DNA_METHYLATION' already exists
## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_ESTROGEN_DEPENDENT_GENE_EXPRESSION'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_HDACS_DEACETYLATE_HISTONES' already
## exists
## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_PRC2_METHYLATES_HISTONES_AND_DNA' already
## exists
## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\RNAseq_geneLevel\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/RNAseq_geneLevel/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \RNAseq_geneLevel\significant\REACTOME_RUNX1_REGULATES_GENES_INVOLVED_IN_MEGAKARYOCYTE_DIFFERENTIATION_AND_PLATELET_FUNCTION'
## already exists
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('DAMAGE',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('SPLIC',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('SENESC',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}total proteomics
ms_total_protein_conditions_contrasts_forGsea = list()
fgsea_ms_total_protein_conditions_contrasts_reactome = list()
fgsea_ms_total_protein_conditions_contrasts_hallmark = list()
for(contrast_idx in 1:length(ms_total_protein_conditions_contrasts)){
curr_contrast = names(ms_total_protein_conditions_contrasts)[contrast_idx]
ms_total_protein_conditions_contrasts_forGsea[[curr_contrast]] = ms_total_protein_conditions_contrasts[[curr_contrast]]$log2FC
names(ms_total_protein_conditions_contrasts_forGsea[[curr_contrast]]) = ms_total_protein_conditions_contrasts[[curr_contrast]]$ensembl_gene_id
if(run_gsea){
fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]] = fgsea(reactome_pathways, ms_total_protein_conditions_contrasts_forGsea[[curr_contrast]],nperm = gsea_nreps, minSize = 1, maxSize = 2000,gseaParam = 1,nproc=1)
fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]] = fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]][order(fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]]$pval),]
fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]][1:20,1:7]
write.table(fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]][,1:7],paste(GSEA_DIR,'/MS_total_protein/protein_',curr_contrast,'_reactome.txt',sep=''),sep='\t',row.names=FALSE)
fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]] = fgsea(hallmark_pathways, ms_total_protein_conditions_contrasts_forGsea[[curr_contrast]],nperm = gsea_nreps, minSize = 1, maxSize = 2000,gseaParam = 1,nproc=1)
fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]] = fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]][order(fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]]$pval),]
fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]][1:20,1:7]
write.table(fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]][,1:7],paste(GSEA_DIR,'/MS_total_protein/protein_',curr_contrast,'_hallmark.txt',sep=''),sep='\t',row.names=FALSE)
} else {
curr_contrast = names(ms_total_protein_conditions_contrasts)[contrast_idx]
fgsea_ms_total_protein_conditions_contrasts_reactome[[curr_contrast]] = read.table(paste(GSEA_DIR,'/MS_total_protein/protein_',curr_contrast,'_reactome.txt',sep=''),sep='\t',header=TRUE)
fgsea_ms_total_protein_conditions_contrasts_hallmark[[curr_contrast]] = read.table(paste(GSEA_DIR,'/MS_total_protein/protein_',curr_contrast,'_hallmark.txt',sep=''),sep='\t',header=TRUE)
message(paste('warning: fgsea was not run, results read from file'))
}
}## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from file
## warning: fgsea was not run, results read from filefgsea_ms_total_protein_reactome_table_1 = merge(fgsea_ms_total_protein_conditions_contrasts_reactome[[1]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_ms_total_protein_conditions_contrasts_reactome[[2]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_ms_total_protein_conditions_contrasts_reactome)[1:2],collapse=NULL,sep=''))
fgsea_ms_total_protein_reactome_table_2 = merge(fgsea_ms_total_protein_conditions_contrasts_reactome[[3]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_ms_total_protein_conditions_contrasts_reactome[[4]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_ms_total_protein_conditions_contrasts_reactome)[3:4],collapse=NULL,sep=''))
fgsea_ms_total_protein_reactome_table_3 = merge(fgsea_ms_total_protein_conditions_contrasts_reactome[[5]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],fgsea_ms_total_protein_conditions_contrasts_reactome[[6]][,c('pathway','pval','padj','ES','NES','nMoreExtreme')],by='pathway',all=TRUE,suffixes=paste('_',names(fgsea_ms_total_protein_conditions_contrasts_reactome)[5:6],collapse=NULL,sep=''))
fgsea_ms_total_protein_reactome_table = merge(fgsea_ms_total_protein_reactome_table_1,fgsea_ms_total_protein_reactome_table_2,by='pathway',all=TRUE)
fgsea_ms_total_protein_reactome_table = merge(fgsea_ms_total_protein_reactome_table,fgsea_ms_total_protein_reactome_table_3,by='pathway',all=TRUE)
fgsea_ms_total_protein_reactome_table_1 = NULL
fgsea_ms_total_protein_reactome_table_2 = NULL
fgsea_ms_total_protein_reactome_table_3 = NULL
fgsea_ms_total_protein_reactome_table = as.data.frame(fgsea_ms_total_protein_reactome_table)
fgsea_ms_total_protein_reactome_table = fgsea_ms_total_protein_reactome_table[order(apply(fgsea_ms_total_protein_reactome_table[,grep('padj',colnames(fgsea_ms_total_protein_reactome_table))],1,min)),]Protein-level enrichment plots
aheatmap(as.matrix(fgsea_ms_total_protein_reactome_table[,grep('NES',colnames(fgsea_ms_total_protein_reactome_table))]))
total_protein_to_plot = total_protein_to_plot[order(total_protein_to_plot$Condition),]
# compare pseudo-volcano plots in "real" vs "WT only" comparisons
plot(fgsea_ms_total_protein_reactome_table$NES_iDUX4t14dox_WTt14dox,-log10(fgsea_ms_total_protein_reactome_table$padj_iDUX4t14dox_WTt14dox),col='red',pch=19)
points(fgsea_ms_total_protein_reactome_table$NES_iDUX4dmso_WTdmso,-log10(fgsea_ms_total_protein_reactome_table$padj_iDUX4dmso_WTdmso),col='blue',pch=19)
points(fgsea_ms_total_protein_reactome_table$NES_WTt14dox_WTdmso,-log10(fgsea_ms_total_protein_reactome_table$padj_WTt14dox_WTdmso),col='black',pch=19)
make_protein_pathway_plots <- function(curr_pathway,category){
dir.create(paste0(GSEA_DIR,'/MS_total_protein/',category,'/'))
dir.create(paste0(GSEA_DIR,'/MS_total_protein/',category,'/',curr_pathway))
# curr_pathway = 'REACTOME_OXIDATIVE_STRESS_INDUCED_SENESCENCE'
curr_pathway_ESmax = max(abs(fgsea_ms_total_protein_reactome_table[fgsea_ms_total_protein_reactome_table$pathway==curr_pathway,grep('ES',colnames(fgsea_ms_total_protein_reactome_table))[c(1:6)*2-1]]))
barplot(t(fgsea_ms_total_protein_reactome_table[fgsea_ms_total_protein_reactome_table$pathway==curr_pathway,grep('NES',colnames(fgsea_ms_total_protein_reactome_table))]),beside=TRUE,names.arg=names(fgsea_RNAseq_lengthScaled_conditions_contrasts_reactome),las=3,main=curr_pathway)
curr_pathway_barplot_data = as.data.frame(t(fgsea_ms_total_protein_reactome_table[fgsea_ms_total_protein_reactome_table$pathway==curr_pathway,grep('NES',colnames(fgsea_ms_total_protein_reactome_table))]))
colnames(curr_pathway_barplot_data) = paste(curr_pathway)
curr_pathway_barplot_data$comparison = row.names(curr_pathway_barplot_data)
curr_pathway_barplot_data$comparison = gsub('NES_','',curr_pathway_barplot_data$comparison)
curr_pathway_barplot_data$NES = curr_pathway_barplot_data[,curr_pathway]
ggplot(curr_pathway_barplot_data,aes(comparison,NES)) + geom_col()
ggsave(paste(GSEA_DIR,'/MS_total_protein/',category,'/',curr_pathway,'/NES_barplot.pdf',sep=''),device='pdf',width=10,height=14)
ep1 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[1]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[1]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[1]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[1]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep2 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[2]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[2]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[2]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[2]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep3 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[3]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[3]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[3]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[3]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep4 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[4]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[4]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[4]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[4]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep5 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[5]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[5]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[5]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[5]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
ep6 = plotEnrichment(reactome_pathways[[curr_pathway]],na.omit(ms_total_protein_conditions_contrasts_forGsea[[6]])) + ggtitle(paste(names(ms_total_protein_conditions_contrasts_forGsea)[[6]],'p_adj',format(fgsea_ms_total_protein_conditions_contrasts_reactome[[6]]$padj[fgsea_ms_total_protein_conditions_contrasts_reactome[[6]]$pathway==curr_pathway],digits=3))) + coord_cartesian(ylim=curr_pathway_ESmax*c(-1.1,1.1))
#grid.arrange(ep1,ep3,ep2,ep4,ep6,ep5,ncol=2)
ep_all_curr_pathway = grid.arrange(ep1,ep3,ep2,ep4,ep6,ep5,ncol=2)
ggsave(paste(GSEA_DIR,'/MS_total_protein/',category,'/',curr_pathway,'/enrichment_plot.pdf',sep=''),plot=ep_all_curr_pathway,device='pdf',width=10,height=14)
curr_pathway_proteins = biomartProteinAnnot_uniprot_select$uniprotswissprot[biomartProteinAnnot_uniprot_select$ensembl_gene_id %in% reactome_pathways[[curr_pathway]]]
if( sum(colnames(total_protein_to_plot) %in% curr_pathway_proteins)>2){
ph=pheatmap(t(log10(total_protein_to_plot[,colnames(total_protein_to_plot) %in% curr_pathway_proteins]+1)),labels_col=total_protein_to_plot$Condition,labels_row=biomartProteinAnnot_uniprot_select$hgnc_symbol[match(as.character(colnames(total_protein_to_plot)[colnames(total_protein_to_plot) %in% curr_pathway_proteins]),as.character(biomartProteinAnnot_uniprot_select$uniprotswissprot))],cluster_cols=FALSE)
ggsave(paste(GSEA_DIR,'/MS_total_protein/',category,'/',curr_pathway,'/proteins_heatmap.pdf',sep=''),plot=ph,device='pdf',width=10,height=14)
ph_scaled=pheatmap(t(log10(total_protein_to_plot[,colnames(total_protein_to_plot) %in% curr_pathway_proteins]+1)),labels_col=total_protein_to_plot$Condition,labels_row=biomartProteinAnnot_uniprot_select$hgnc_symbol[match(as.character(colnames(total_protein_to_plot)[colnames(total_protein_to_plot) %in% curr_pathway_proteins]),as.character(biomartProteinAnnot_uniprot_select$uniprotswissprot))],cluster_cols=FALSE,scale='row')
ggsave(paste(GSEA_DIR,'/MS_total_protein/',category,'/',curr_pathway,'/proteins_heatmap_scaled.pdf',sep=''),plot=ph_scaled,device='pdf',width=10,height=14)
}
}
fgsea_ms_total_protein_reactome_table[order(apply(fgsea_ms_total_protein_reactome_table[,grep('padj',colnames(fgsea_ms_total_protein_reactome_table))],1,min)),]## pathway
## 21 REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3
## 24 REACTOME_ACTIVATION_OF_ANTERIOR_HOX_GENES_IN_HINDBRAIN_DEVELOPMENT_DURING_EARLY_EMBRYOGENESIS
## 108 REACTOME_B_WICH_COMPLEX_POSITIVELY_REGULATES_RRNA_EXPRESSION
## 109 REACTOME_BASE_EXCISION_REPAIR
## 110 REACTOME_BASE_EXCISION_REPAIR_AP_SITE_FORMATION
## 170 REACTOME_CELLULAR_SENESCENCE
## 181 REACTOME_CHROMOSOME_MAINTENANCE
## 205 REACTOME_CONDENSATION_OF_PROPHASE_CHROMOSOMES
## 275 REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE
## 299 REACTOME_DNA_DAMAGE_TELOMERE_STRESS_INDUCED_SENESCENCE
## 300 REACTOME_DNA_METHYLATION
## 341 REACTOME_EPIGENETIC_REGULATION_OF_GENE_EXPRESSION
## 346 REACTOME_ERCC6_CSB_AND_EHMT2_G9A_POSITIVELY_REGULATE_RRNA_EXPRESSION
## 404 REACTOME_FORMATION_OF_THE_BETA_CATENIN_TCF_TRANSACTIVATING_COMPLEX
## 483 REACTOME_HDACS_DEACETYLATE_HISTONES
## 487 REACTOME_HDMS_DEMETHYLATE_HISTONES
## 504 REACTOME_HOMOLOGY_DIRECTED_REPAIR
## 521 REACTOME_INHIBITION_OF_DNA_RECOMBINATION_AT_TELOMERE
## 624 REACTOME_MEIOSIS
## 625 REACTOME_MEIOTIC_RECOMBINATION
## 709 REACTOME_NEGATIVE_EPIGENETIC_REGULATION_OF_RRNA_EXPRESSION
## 788 REACTOME_OXIDATIVE_STRESS_INDUCED_SENESCENCE
## 847 REACTOME_POSITIVE_EPIGENETIC_REGULATION_OF_RRNA_EXPRESSION
## 855 REACTOME_PRC2_METHYLATES_HISTONES_AND_DNA
## 856 REACTOME_PRE_NOTCH_EXPRESSION_AND_PROCESSING
## 865 REACTOME_PROCESSING_OF_DNA_DOUBLE_STRAND_BREAK_ENDS
## 906 REACTOME_RECOGNITION_AND_ASSOCIATION_OF_DNA_GLYCOSYLASE_WITH_SITE_CONTAINING_AN_AFFECTED_PURINE
## 958 REACTOME_REPRODUCTION
## 980 REACTOME_RHO_GTPASES_ACTIVATE_PKNS
## 986 REACTOME_RNA_POLYMERASE_I_PROMOTER_ESCAPE
## 987 REACTOME_RNA_POLYMERASE_I_TRANSCRIPTION
## 1013 REACTOME_RUNX1_REGULATES_GENES_INVOLVED_IN_MEGAKARYOCYTE_DIFFERENTIATION_AND_PLATELET_FUNCTION
## 1131 REACTOME_SIRT1_NEGATIVELY_REGULATES_RRNA_EXPRESSION
## 1213 REACTOME_TELOMERE_MAINTENANCE
## 1272 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_SMALL_RNAS
## 1275 REACTOME_TRANSCRIPTIONAL_REGULATION_OF_GRANULOPOIESIS
## 355 REACTOME_ESR_MEDIATED_SIGNALING
## 626 REACTOME_MEIOTIC_SYNAPSIS
## 744 REACTOME_NONHOMOLOGOUS_END_JOINING_NHEJ
## 431 REACTOME_G2_M_DNA_DAMAGE_CHECKPOINT
## 985 REACTOME_RMTS_METHYLATE_HISTONE_ARGININES
## 405 REACTOME_FORMATION_OF_THE_CORNIFIED_ENVELOPE
## 479 REACTOME_HATS_ACETYLATE_HISTONES
## 520 REACTOME_INFLUENZA_INFECTION
## 587 REACTOME_KERATINIZATION
## 832 REACTOME_PKMTS_METHYLATE_HISTONE_LYSINES
## 162 REACTOME_CELL_CYCLE_MITOTIC
## 161 REACTOME_CELL_CYCLE_CHECKPOINTS
## 430 REACTOME_G2_M_CHECKPOINTS
## 160 REACTOME_CELL_CYCLE
## 482 REACTOME_HCMV_LATE_EVENTS
## 647 REACTOME_METABOLISM_OF_RNA
## 1031 REACTOME_S_PHASE
## 357 REACTOME_ESTROGEN_DEPENDENT_GENE_EXPRESSION
## 1048 REACTOME_SENESCENCE_ASSOCIATED_SECRETORY_PHENOTYPE_SASP
## 366 REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION
## 1144 REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE
## 180 REACTOME_CHROMATIN_MODIFYING_ENZYMES
## 677 REACTOME_MITOTIC_PROPHASE
## 362 REACTOME_EUKARYOTIC_TRANSLATION_INITIATION
## 932 REACTOME_REGULATION_OF_INSULIN_LIKE_GROWTH_FACTOR_IGF_TRANSPORT_AND_UPTAKE_BY_INSULIN_LIKE_GROWTH_FACTOR_BINDING_PROTEINS_IGFBPS
## 1155 REACTOME_SUMOYLATION_OF_CHROMATIN_ORGANIZATION_PROTEINS
## 1111 REACTOME_SIGNALING_BY_NUCLEAR_RECEPTORS
## 1154 REACTOME_SUMOYLATION
## 973 REACTOME_RHO_GTPASE_EFFECTORS
## 967 REACTOME_RESPONSE_TO_ELEVATED_PLATELET_CYTOSOLIC_CA2
## 1037 REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA
## 745 REACTOME_NONSENSE_MEDIATED_DECAY_NMD
## 863 REACTOME_PROCESSING_OF_CAPPED_INTRON_CONTAINING_PRE_MRNA
## 44 REACTOME_ACTIVATION_OF_THE_PRE_REPLICATIVE_COMPLEX
## 673 REACTOME_MITOTIC_G1_PHASE_AND_G1_S_TRANSITION
## 200 REACTOME_COLLAGEN_FORMATION
## 1006 REACTOME_RRNA_PROCESSING
## 480 REACTOME_HCMV_EARLY_EVENTS
## 688 REACTOME_MRNA_SPLICING
## 965 REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY
## 25 REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS
## 1015 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_DIFFERENTIATION_OF_HSCS
## 361 REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION
## 836 REACTOME_PLASMA_LIPOPROTEIN_REMODELING
## 1041 REACTOME_SELENOAMINO_ACID_METABOLISM
## 304 REACTOME_DNA_STRAND_ELONGATION
## 1319 REACTOME_UNWINDING_OF_DNA
## 204 REACTOME_CONDENSATION_OF_PROMETAPHASE_CHROMOSOMES
## 734 REACTOME_NEUTROPHIL_DEGRANULATION
## 301 REACTOME_DNA_REPLICATION
## 609 REACTOME_M_PHASE
## 895 REACTOME_RAB_GERANYLGERANYLATION
## 197 REACTOME_COLLAGEN_BIOSYNTHESIS_AND_MODIFYING_ENZYMES
## 535 REACTOME_INTEGRIN_CELL_SURFACE_INTERACTIONS
## 1208 REACTOME_TCF_DEPENDENT_SIGNALING_IN_RESPONSE_TO_WNT
## 1127 REACTOME_SIGNALING_BY_WNT
## 124 REACTOME_BINDING_AND_UPTAKE_OF_LIGANDS_BY_SCAVENGER_RECEPTORS
## 481 REACTOME_HCMV_INFECTION
## 184 REACTOME_CHYLOMICRON_REMODELING
## 1100 REACTOME_SIGNALING_BY_NOTCH
## 495 REACTOME_HEMOSTASIS
## 1291 REACTOME_TRANSPORT_OF_INORGANIC_CATIONS_ANIONS_AND_AMINO_ACIDS_OLIGOPEPTIDES
## 324 REACTOME_ECM_PROTEOGLYCANS
## 837 REACTOME_PLATELET_ACTIVATION_SIGNALING_AND_AGGREGATION
## 182 REACTOME_CHYLOMICRON_ASSEMBLY
## 1269 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_RUNX1
## 921 REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS
## 1035 REACTOME_SCAVENGING_BY_CLASS_B_RECEPTORS
## 689 REACTOME_MRNA_SPLICING_MINOR_PATHWAY
## 568 REACTOME_INTRAFLAGELLAR_TRANSPORT
## 563 REACTOME_INTERLEUKIN_7_SIGNALING
## 669 REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT
## 95 REACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES
## 273 REACTOME_DEGRADATION_OF_THE_EXTRACELLULAR_MATRIX
## 1279 REACTOME_TRANSLATION
## 203 REACTOME_COMPLEMENT_CASCADE
## 42 REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S
## 780 REACTOME_ONCOGENIC_MAPK_SIGNALING
## 642 REACTOME_METABOLISM_OF_FAT_SOLUBLE_VITAMINS
## 198 REACTOME_COLLAGEN_CHAIN_TRIMERIZATION
## 435 REACTOME_GABA_B_RECEPTOR_ACTIVATION
## 724 REACTOME_NERVOUS_SYSTEM_DEVELOPMENT
## 894 REACTOME_RAB_GEFS_EXCHANGE_GTP_FOR_GDP_ON_RABS
## 303 REACTOME_DNA_REPLICATION_PRE_INITIATION
## 886 REACTOME_PTK6_REGULATES_RTKS_AND_THEIR_EFFECTORS_AKT1_AND_DOK1
## 1067 REACTOME_SIGNALING_BY_BRAF_AND_RAF_FUSIONS
## 801 REACTOME_PENTOSE_PHOSPHATE_PATHWAY
## 93 REACTOME_ASSEMBLY_AND_CELL_SURFACE_PRESENTATION_OF_NMDA_RECEPTORS
## 199 REACTOME_COLLAGEN_DEGRADATION
## 676 REACTOME_MITOTIC_PROMETAPHASE
## 537 REACTOME_INTERACTION_BETWEEN_L1_AND_ANKYRINS
## 701 REACTOME_NCAM1_INTERACTIONS
## 1119 REACTOME_SIGNALING_BY_ROBO_RECEPTORS
## 320 REACTOME_E2F_ENABLED_INHIBITION_OF_PRE_REPLICATION_COMPLEX_FORMATION
## 510 REACTOME_HSP90_CHAPERONE_CYCLE_FOR_STEROID_HORMONE_RECEPTORS_SHR
## 211 REACTOME_CONVERSION_FROM_APC_C_CDC20_TO_APC_C_CDH1_IN_LATE_ANAPHASE
## 402 REACTOME_FORMATION_OF_SENESCENCE_ASSOCIATED_HETEROCHROMATIN_FOCI_SAHF
## 331 REACTOME_ELASTIC_FIBRE_FORMATION
## 442 REACTOME_GAP_JUNCTION_ASSEMBLY
## 1289 REACTOME_TRANSPORT_OF_CONNEXONS_TO_THE_PLASMA_MEMBRANE
## 1311 REACTOME_TRYPTOPHAN_CATABOLISM
## 851 REACTOME_POTASSIUM_CHANNELS
## 787 REACTOME_OVARIAN_TUMOR_DOMAIN_PROTEASES
## 1169 REACTOME_SWITCHING_OF_ORIGINS_TO_A_POST_REPLICATIVE_STATE
## 680 REACTOME_MODULATION_BY_MTB_OF_HOST_IMMUNE_SYSTEM
## 500 REACTOME_HIV_LIFE_CYCLE
## 486 REACTOME_HDL_REMODELING
## 55 REACTOME_ADRENALINE_NORADRENALINE_INHIBITS_INSULIN_SECRETION
## 364 REACTOME_EXTENSION_OF_TELOMERES
## 91 REACTOME_ASPARAGINE_N_LINKED_GLYCOSYLATION
## 485 REACTOME_HDL_CLEARANCE
## 1096 REACTOME_SIGNALING_BY_MET
## 52 REACTOME_ADORA2B_MEDIATED_ANTI_INFLAMMATORY_CYTOKINES_PRODUCTION
## 640 REACTOME_METABOLISM_OF_CARBOHYDRATES
## 861 REACTOME_PREVENTION_OF_PHAGOSOMAL_LYSOSOMAL_FUSION
## 425 REACTOME_G_BETA_GAMMA_SIGNALLING_THROUGH_PI3KGAMMA
## 54 REACTOME_ADP_SIGNALLING_THROUGH_P2Y_PURINOCEPTOR_12
## 655 REACTOME_METALLOTHIONEINS_BIND_METALS
## 436 REACTOME_GABA_RECEPTOR_ACTIVATION
## 1329 REACTOME_VISUAL_PHOTOTRANSDUCTION
## 811 REACTOME_PHENYLALANINE_METABOLISM
## 1285 REACTOME_TRANSLOCATION_OF_SLC2A4_GLUT4_TO_THE_PLASMA_MEMBRANE
## 94 REACTOME_ASSEMBLY_OF_ACTIVE_LPL_AND_LIPC_LIPASE_COMPLEXES
## 212 REACTOME_COOPERATION_OF_PREFOLDIN_AND_TRIC_CCT_IN_ACTIN_AND_TUBULIN_FOLDING
## 627 REACTOME_MEMBRANE_TRAFFICKING
## 444 REACTOME_GAP_JUNCTION_TRAFFICKING_AND_REGULATION
## 896 REACTOME_RAB_REGULATION_OF_TRAFFICKING
## 1061 REACTOME_SIGNAL_AMPLIFICATION
## 65 REACTOME_AMINO_ACID_TRANSPORT_ACROSS_THE_PLASMA_MEMBRANE
## 228 REACTOME_CROSSLINKING_OF_COLLAGEN_FIBRILS
## 794 REACTOME_P75NTR_REGULATES_AXONOGENESIS
## 976 REACTOME_RHO_GTPASES_ACTIVATE_IQGAPS
## 781 REACTOME_ORC1_REMOVAL_FROM_CHROMATIN
## 681 REACTOME_MOLECULES_ASSOCIATED_WITH_ELASTIC_FIBRES
## 1299 REACTOME_TRANSPORT_TO_THE_GOLGI_AND_SUBSEQUENT_MODIFICATION
## 1183 REACTOME_SYNTHESIS_OF_IP2_IP_AND_INS_IN_THE_CYTOSOL
## 165 REACTOME_CELL_JUNCTION_ORGANIZATION
## 59 REACTOME_AKT_PHOSPHORYLATES_TARGETS_IN_THE_CYTOSOL
## 407 REACTOME_FORMATION_OF_TUBULIN_FOLDING_INTERMEDIATES_BY_CCT_TRIC
## 7 REACTOME_ABC_TRANSPORTERS_IN_LIPID_HOMEOSTASIS
## 797 REACTOME_PCNA_DEPENDENT_LONG_PATCH_BASE_EXCISION_REPAIR
## 368 REACTOME_FACTORS_INVOLVED_IN_MEGAKARYOCYTE_DEVELOPMENT_AND_PLATELET_PRODUCTION
## 922 REACTOME_REGULATION_OF_FOXO_TRANSCRIPTIONAL_ACTIVITY_BY_ACETYLATION
## 426 REACTOME_G_PROTEIN_BETA_GAMMA_SIGNALLING
## 566 REACTOME_INTRA_GOLGI_AND_RETROGRADE_GOLGI_TO_ER_TRAFFIC
## 834 REACTOME_PLASMA_LIPOPROTEIN_ASSEMBLY_REMODELING_AND_CLEARANCE
## 1274 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_VENTX
## 429 REACTOME_G1_S_SPECIFIC_TRANSCRIPTION
## 1095 REACTOME_SIGNALING_BY_MEMBRANE_TETHERED_FUSIONS_OF_PDGFRA_OR_PDGFRB
## 571 REACTOME_ION_HOMEOSTASIS
## 583 REACTOME_JOSEPHIN_DOMAIN_DUBS
## 71 REACTOME_ANTI_INFLAMMATORY_RESPONSE_FAVOURING_LEISHMANIA_PARASITE_INFECTION
## 817 REACTOME_PHOSPHORYLATION_OF_THE_APC_C
## 1039 REACTOME_SEALING_OF_THE_NUCLEAR_ENVELOPE_NE_BY_ESCRT_III
## 593 REACTOME_LAGGING_STRAND_SYNTHESIS
## 523 REACTOME_INHIBITION_OF_THE_PROTEOLYTIC_ACTIVITY_OF_APC_C_REQUIRED_FOR_THE_ONSET_OF_ANAPHASE_BY_MITOTIC_SPINDLE_CHECKPOINT_COMPONENTS
## 952 REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_METHYLATION
## 1077 REACTOME_SIGNALING_BY_FGFR
## 1081 REACTOME_SIGNALING_BY_FGFR2
## 158 REACTOME_CELL_CELL_COMMUNICATION
## 977 REACTOME_RHO_GTPASES_ACTIVATE_KTN1
## 939 REACTOME_REGULATION_OF_PTEN_GENE_TRANSCRIPTION
## 675 REACTOME_MITOTIC_METAPHASE_AND_ANAPHASE
## 290 REACTOME_DISEASES_OF_METABOLISM
## 742 REACTOME_NON_INTEGRIN_MEMBRANE_ECM_INTERACTIONS
## 223 REACTOME_CREB1_PHOSPHORYLATION_THROUGH_THE_ACTIVATION_OF_CAMKII_CAMKK_CAMKIV_CASCASDE
## 23 REACTOME_ACTIVATION_OF_AMPK_DOWNSTREAM_OF_NMDARS
## 912 REACTOME_RECYCLING_PATHWAY_OF_L1
## 323 REACTOME_EARLY_PHASE_OF_HIV_LIFE_CYCLE
## 610 REACTOME_MAP2K_AND_MAPK_ACTIVATION
## 960 REACTOME_RESOLUTION_OF_AP_SITES_VIA_THE_MULTIPLE_NUCLEOTIDE_PATCH_REPLACEMENT_PATHWAY
## 1267 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_E2F6
## 176 REACTOME_CHOLINE_CATABOLISM
## 833 REACTOME_PLASMA_LIPOPROTEIN_ASSEMBLY
## 343 REACTOME_ER_TO_GOLGI_ANTEROGRADE_TRANSPORT
## 949 REACTOME_REGULATION_OF_TP53_ACTIVITY
## 287 REACTOME_DISEASES_ASSOCIATED_WITH_O_GLYCOSYLATION_OF_PROTEINS
## 83 REACTOME_APOPTOSIS_INDUCED_DNA_FRAGMENTATION
## 959 REACTOME_RESOLUTION_OF_ABASIC_SITES_AP_SITES
## 401 REACTOME_FORMATION_OF_RNA_POL_II_ELONGATION_COMPLEX
## 953 REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_PHOSPHORYLATION
## 1097 REACTOME_SIGNALING_BY_MODERATE_KINASE_ACTIVITY_BRAF_MUTANTS
## 349 REACTOME_ERYTHROCYTES_TAKE_UP_CARBON_DIOXIDE_AND_RELEASE_OXYGEN
## 945 REACTOME_REGULATION_OF_RUNX1_EXPRESSION_AND_ACTIVITY
## 687 REACTOME_MRNA_EDITING_C_TO_U_CONVERSION
## 899 REACTOME_RAP1_SIGNALLING
## 1159 REACTOME_SUMOYLATION_OF_IMMUNE_RESPONSE_PROTEINS
## 35 REACTOME_ACTIVATION_OF_NMDA_RECEPTORS_AND_POSTSYNAPTIC_EVENTS
## 53 REACTOME_ADP_SIGNALLING_THROUGH_P2Y_PURINOCEPTOR_1
## 762 REACTOME_NRIF_SIGNALS_CELL_DEATH_FROM_THE_NUCLEUS
## 853 REACTOME_POU5F1_OCT4_SOX2_NANOG_REPRESS_GENES_RELATED_TO_DIFFERENTIATION
## 225 REACTOME_CRMPS_IN_SEMA3A_SIGNALING
## 1210 REACTOME_TELOMERE_C_STRAND_LAGGING_STRAND_SYNTHESIS
## 763 REACTOME_NS1_MEDIATED_EFFECTS_ON_HOST_PATHWAYS
## 1046 REACTOME_SEMA4D_MEDIATED_INHIBITION_OF_CELL_ATTACHMENT_AND_MIGRATION
## 1098 REACTOME_SIGNALING_BY_MRAS_COMPLEX_MUTANTS
## 670 REACTOME_MITOCHONDRIAL_TRANSLATION
## 890 REACTOME_PYRIMIDINE_SALVAGE
## 58 REACTOME_AGGREPHAGY
## 376 REACTOME_FCERI_MEDIATED_CA_2_MOBILIZATION
## 519 REACTOME_INFECTION_WITH_MYCOBACTERIUM_TUBERCULOSIS
## 569 REACTOME_INTRINSIC_PATHWAY_OF_FIBRIN_CLOT_FORMATION
## 905 REACTOME_RECEPTOR_TYPE_TYROSINE_PROTEIN_PHOSPHATASES
## 172 REACTOME_CHAPERONE_MEDIATED_AUTOPHAGY
## 342 REACTOME_ER_QUALITY_CONTROL_COMPARTMENT_ERQC
## 526 REACTOME_INLA_MEDIATED_ENTRY_OF_LISTERIA_MONOCYTOGENES_INTO_HOST_CELLS
## 467 REACTOME_GLYCOSAMINOGLYCAN_METABOLISM
## 502 REACTOME_HIV_TRANSCRIPTION_INITIATION
## 1171 REACTOME_SYNDECAN_INTERACTIONS
## 237 REACTOME_CYTOSOLIC_SULFONATION_OF_SMALL_MOLECULES
## 729 REACTOME_NEURONAL_SYSTEM
## 1133 REACTOME_SLC_MEDIATED_TRANSMEMBRANE_TRANSPORT
## 1287 REACTOME_TRANSPORT_AND_SYNTHESIS_OF_PAPS
## 473 REACTOME_GP1B_IX_V_ACTIVATION_SIGNALLING
## 1286 REACTOME_TRANSMISSION_ACROSS_CHEMICAL_SYNAPSES
## 773 REACTOME_NUCLEOTIDE_EXCISION_REPAIR
## 11 REACTOME_ACETYLCHOLINE_NEUROTRANSMITTER_RELEASE_CYCLE
## 399 REACTOME_FORMATION_OF_FIBRIN_CLOT_CLOTTING_CASCADE
## 650 REACTOME_METABOLISM_OF_VITAMINS_AND_COFACTORS
## 746 REACTOME_NOREPINEPHRINE_NEUROTRANSMITTER_RELEASE_CYCLE
## 1052 REACTOME_SEROTONIN_NEUROTRANSMITTER_RELEASE_CYCLE
## 306 REACTOME_DOWNREGULATION_OF_ERBB2_ERBB3_SIGNALING
## 256 REACTOME_DEFECTIVE_CHST6_CAUSES_MCDC1
## 263 REACTOME_DEFECTIVE_ST3GAL3_CAUSES_MCT12_AND_EIEE15
## 1195 REACTOME_SYNTHESIS_OF_PIPS_AT_THE_GOLGI_MEMBRANE
## 465 REACTOME_GLYCOGEN_SYNTHESIS
## 79 REACTOME_APC_C_MEDIATED_DEGRADATION_OF_CELL_CYCLE_PROTEINS
## 97 REACTOME_ASSEMBLY_OF_THE_PRE_REPLICATIVE_COMPLEX
## 427 REACTOME_G0_AND_EARLY_G1
## 379 REACTOME_FCGAMMA_RECEPTOR_FCGR_DEPENDENT_PHAGOCYTOSIS
## 572 REACTOME_ION_TRANSPORT_BY_P_TYPE_ATPASES
## 1337 REACTOME_VOLTAGE_GATED_POTASSIUM_CHANNELS
## 432 REACTOME_G2_M_DNA_REPLICATION_CHECKPOINT
## 149 REACTOME_CASPASE_MEDIATED_CLEAVAGE_OF_CYTOSKELETAL_PROTEINS
## 1327 REACTOME_VESICLE_MEDIATED_TRANSPORT
## 1284 REACTOME_TRANSLESION_SYNTHESIS_BY_Y_FAMILY_DNA_POLYMERASES_BYPASSES_LESIONS_ON_DNA_TEMPLATE
## 1321 REACTOME_UPTAKE_AND_FUNCTION_OF_ANTHRAX_TOXINS
## 848 REACTOME_POST_CHAPERONIN_TUBULIN_FOLDING_PATHWAY
## 1310 REACTOME_TRP_CHANNELS
## 570 REACTOME_INWARDLY_RECTIFYING_K_CHANNELS
## 68 REACTOME_ANCHORING_FIBRIL_FORMATION
## 968 REACTOME_RESPONSE_TO_METAL_IONS
## 189 REACTOME_CLASS_I_PEROXISOMAL_MEMBRANE_PROTEIN_IMPORT
## 1293 REACTOME_TRANSPORT_OF_MATURE_TRANSCRIPT_TO_CYTOPLASM
## 796 REACTOME_PARASITE_INFECTION
## 50 REACTOME_ADENYLATE_CYCLASE_INHIBITORY_PATHWAY
## 1276 REACTOME_TRANSCRIPTIONAL_REGULATION_OF_PLURIPOTENT_STEM_CELLS
## 1050 REACTOME_SEPARATION_OF_SISTER_CHROMATIDS
## 185 REACTOME_CILIUM_ASSEMBLY
## 1 REACTOME_2_LTR_CIRCLE_FORMATION
## 732 REACTOME_NEUROTRANSMITTER_RECEPTORS_AND_POSTSYNAPTIC_SIGNAL_TRANSMISSION
## 931 REACTOME_REGULATION_OF_INNATE_IMMUNE_RESPONSES_TO_CYTOSOLIC_DNA
## 321 REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_REPLICATION
## 87 REACTOME_AQUAPORIN_MEDIATED_TRANSPORT
## 451 REACTOME_GLUCAGON_SIGNALING_IN_METABOLIC_REGULATION
## 808 REACTOME_PHASE_3_RAPID_REPOLARISATION
## 1324 REACTOME_VASOPRESSIN_REGULATES_RENAL_WATER_HOMEOSTASIS_VIA_AQUAPORINS
## 141 REACTOME_CAMK_IV_MEDIATED_PHOSPHORYLATION_OF_CREB
## 860 REACTOME_PRESYNAPTIC_FUNCTION_OF_KAINATE_RECEPTORS
## 844 REACTOME_POLO_LIKE_KINASE_MEDIATED_EVENTS
## 289 REACTOME_DISEASES_OF_GLYCOSYLATION
## 1292 REACTOME_TRANSPORT_OF_MATURE_MRNAS_DERIVED_FROM_INTRONLESS_TRANSCRIPTS
## 891 REACTOME_PYRUVATE_METABOLISM
## 365 REACTOME_EXTRA_NUCLEAR_ESTROGEN_SIGNALING
## 933 REACTOME_REGULATION_OF_INSULIN_SECRETION
## 679 REACTOME_MITOTIC_TELOPHASE_CYTOKINESIS
## 700 REACTOME_NCAM_SIGNALING_FOR_NEURITE_OUT_GROWTH
## 214 REACTOME_COPI_INDEPENDENT_GOLGI_TO_ER_RETROGRADE_TRAFFIC
## 1261 REACTOME_TRANSCRIPTION_COUPLED_NUCLEOTIDE_EXCISION_REPAIR_TC_NER
## 1264 REACTOME_TRANSCRIPTION_OF_THE_HIV_GENOME
## 423 REACTOME_G_ALPHA_Z_SIGNALLING_EVENTS
## 717 REACTOME_NEGATIVE_REGULATION_OF_MET_ACTIVITY
## 831 REACTOME_PKA_MEDIATED_PHOSPHORYLATION_OF_KEY_METABOLIC_FACTORS
## 1083 REACTOME_SIGNALING_BY_FGFR2_IN_DISEASE
## 1247 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_CYTOCHROME_C_RELEASE
## 1215 REACTOME_TERMINATION_OF_TRANSLESION_DNA_SYNTHESIS
## 1282 REACTOME_TRANSLESION_SYNTHESIS_BY_POLH
## 578 REACTOME_IRF3_MEDIATED_INDUCTION_OF_TYPE_I_IFN
## 1010 REACTOME_RUNX1_INTERACTS_WITH_CO_FACTORS_WHOSE_PRECISE_EFFECT_ON_RUNX1_TARGETS_IS_NOT_KNOWN
## 1193 REACTOME_SYNTHESIS_OF_PIPS_AT_THE_EARLY_ENDOSOME_MEMBRANE
## 250 REACTOME_DEFECTIVE_B4GALT1_CAUSES_B4GALT1_CDG_CDG_2D
## 1158 REACTOME_SUMOYLATION_OF_DNA_REPLICATION_PROTEINS
## 8 REACTOME_ABERRANT_REGULATION_OF_MITOTIC_EXIT_IN_CANCER_DUE_TO_RB1_DEFECTS
## 678 REACTOME_MITOTIC_SPINDLE_CHECKPOINT
## 215 REACTOME_COPI_MEDIATED_ANTEROGRADE_TRANSPORT
## 450 REACTOME_GLUCAGON_LIKE_PEPTIDE_1_GLP1_REGULATES_INSULIN_SECRETION
## 484 REACTOME_HDL_ASSEMBLY
## 86 REACTOME_APOPTOTIC_EXECUTION_PHASE
## 963 REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION
## 966 REACTOME_RESPONSE_OF_MTB_TO_PHAGOCYTOSIS
## 594 REACTOME_LAMININ_INTERACTIONS
## 830 REACTOME_PKA_MEDIATED_PHOSPHORYLATION_OF_CREB
## 1112 REACTOME_SIGNALING_BY_PDGF
## 1116 REACTOME_SIGNALING_BY_RECEPTOR_TYROSINE_KINASES
## 1243 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_CELL_CYCLE_GENES
## 962 REACTOME_RESOLUTION_OF_D_LOOP_STRUCTURES_THROUGH_SYNTHESIS_DEPENDENT_STRAND_ANNEALING_SDSA
## 540 REACTOME_INTERACTIONS_OF_VPR_WITH_HOST_CELLULAR_PROTEINS
## 592 REACTOME_L1CAM_INTERACTIONS
## 660 REACTOME_MISCELLANEOUS_SUBSTRATES
## 868 REACTOME_PROCESSIVE_SYNTHESIS_ON_THE_C_STRAND_OF_THE_TELOMERE
## 1174 REACTOME_SYNTHESIS_OF_16_20_HYDROXYEICOSATETRAENOIC_ACIDS_HETE
## 190 REACTOME_CLATHRIN_MEDIATED_ENDOCYTOSIS
## 845 REACTOME_POLYMERASE_SWITCHING
## 846 REACTOME_POLYMERASE_SWITCHING_ON_THE_C_STRAND_OF_THE_TELOMERE
## 515 REACTOME_IKK_COMPLEX_RECRUITMENT_MEDIATED_BY_RIP1
## 557 REACTOME_INTERLEUKIN_3_INTERLEUKIN_5_AND_GM_CSF_SIGNALING
## 1230 REACTOME_TICAM1_RIP1_MEDIATED_IKK_COMPLEX_RECRUITMENT
## 857 REACTOME_PRE_NOTCH_PROCESSING_IN_GOLGI
## 686 REACTOME_MRNA_EDITING
## 589 REACTOME_KILLING_MECHANISMS
## 433 REACTOME_G2_PHASE
## 339 REACTOME_EPHB_MEDIATED_FORWARD_SIGNALING
## 284 REACTOME_DISEASES_ASSOCIATED_WITH_GLYCOSAMINOGLYCAN_METABOLISM
## 1139 REACTOME_SNRNP_ASSEMBLY
## 870 REACTOME_PROLACTIN_RECEPTOR_SIGNALING
## 816 REACTOME_PHOSPHORYLATION_OF_EMI1
## 374 REACTOME_FBXW7_MUTANTS_AND_NOTCH1_IN_CANCER
## 574 REACTOME_IRAK1_RECRUITS_IKK_COMPLEX
## 974 REACTOME_RHO_GTPASES_ACTIVATE_CIT
## 472 REACTOME_GOLGI_TO_ER_RETROGRADE_TRANSPORT
## 41 REACTOME_ACTIVATION_OF_THE_AP_1_FAMILY_OF_TRANSCRIPTION_FACTORS
## 130 REACTOME_BLOOD_GROUP_SYSTEMS_BIOSYNTHESIS
## 1224 REACTOME_THE_ROLE_OF_GTSE1_IN_G2_M_PROGRESSION_AFTER_G2_CHECKPOINT
## 982 REACTOME_RHO_GTPASES_ACTIVATE_ROCKS
## 1045 REACTOME_SEMA4D_INDUCED_CELL_MIGRATION_AND_GROWTH_CONE_COLLAPSE
## 1156 REACTOME_SUMOYLATION_OF_DNA_DAMAGE_RESPONSE_AND_REPAIR_PROTEINS
## 877 REACTOME_PROTEIN_REPAIR
## 884 REACTOME_PTK6_REGULATES_PROTEINS_INVOLVED_IN_RNA_PROCESSING
## 196 REACTOME_COHESIN_LOADING_ONTO_CHROMATIN
## 1202 REACTOME_SYNTHESIS_OF_VERY_LONG_CHAIN_FATTY_ACYL_COAS
## 403 REACTOME_FORMATION_OF_TC_NER_PRE_INCISION_COMPLEX
## 157 REACTOME_CDC6_ASSOCIATION_WITH_THE_ORC_ORIGIN_COMPLEX
## 706 REACTOME_NEF_MEDIATED_CD8_DOWN_REGULATION
## 1212 REACTOME_TELOMERE_EXTENSION_BY_TELOMERASE
## 586 REACTOME_KERATAN_SULFATE_KERATIN_METABOLISM
## 231 REACTOME_CYCLIN_A_B1_B2_ASSOCIATED_EVENTS_DURING_G2_M_TRANSITION
## 539 REACTOME_INTERACTIONS_OF_REV_WITH_HOST_CELLULAR_PROTEINS
## 424 REACTOME_G_BETA_GAMMA_SIGNALLING_THROUGH_CDC42
## 585 REACTOME_KERATAN_SULFATE_DEGRADATION
## 202 REACTOME_COMPETING_ENDOGENOUS_RNAS_CERNAS_REGULATE_PTEN_TRANSLATION
## 941 REACTOME_REGULATION_OF_PTEN_MRNA_TRANSLATION
## 1117 REACTOME_SIGNALING_BY_RETINOIC_ACID
## 1277 REACTOME_TRANSCRIPTIONAL_REGULATION_OF_WHITE_ADIPOCYTE_DIFFERENTIATION
## 1147 REACTOME_STING_MEDIATED_INDUCTION_OF_HOST_IMMUNE_RESPONSES
## 874 REACTOME_PROTEIN_LOCALIZATION
## 805 REACTOME_PEROXISOMAL_LIPID_METABOLISM
## 344 REACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING
## 457 REACTOME_GLUTAMATE_NEUROTRANSMITTER_RELEASE_CYCLE
## 999 REACTOME_ROLE_OF_LAT2_NTAL_LAB_ON_CALCIUM_MOBILIZATION
## 389 REACTOME_FGFR2_MUTANT_RECEPTOR_ACTIVATION
## 1082 REACTOME_SIGNALING_BY_FGFR2_IIIA_TM
## 1170 REACTOME_SYNAPTIC_ADHESION_LIKE_MOLECULES
## 909 REACTOME_RECRUITMENT_OF_NUMA_TO_MITOTIC_CENTROSOMES
## 78 REACTOME_APC_C_CDH1_MEDIATED_DEGRADATION_OF_CDC20_AND_OTHER_APC_C_CDH1_TARGETED_PROTEINS_IN_LATE_MITOSIS_EARLY_G1
## 771 REACTOME_NUCLEAR_SIGNALING_BY_ERBB4
## 498 REACTOME_HIV_ELONGATION_ARREST_AND_RECOVERY
## 509 REACTOME_HSF1_DEPENDENT_TRANSACTIVATION
## 534 REACTOME_INTEGRATION_OF_PROVIRUS
## 629 REACTOME_MET_ACTIVATES_PTK2_SIGNALING
## 1298 REACTOME_TRANSPORT_OF_VITAMINS_NUCLEOSIDES_AND_RELATED_MOLECULES
## 337 REACTOME_EPH_EPHRIN_SIGNALING
## 1227 REACTOME_THROMBOXANE_SIGNALLING_THROUGH_TP_RECEPTOR
## 319 REACTOME_DUAL_INCISION_IN_TC_NER
## 1185 REACTOME_SYNTHESIS_OF_KETONE_BODIES
## 213 REACTOME_COPI_DEPENDENT_GOLGI_TO_ER_RETROGRADE_TRAFFIC
## 350 REACTOME_ERYTHROCYTES_TAKE_UP_OXYGEN_AND_RELEASE_CARBON_DIOXIDE
## 1178 REACTOME_SYNTHESIS_OF_BILE_ACIDS_AND_BILE_SALTS_VIA_24_HYDROXYCHOLESTEROL
## 1179 REACTOME_SYNTHESIS_OF_BILE_ACIDS_AND_BILE_SALTS_VIA_27_HYDROXYCHOLESTEROL
## 1256 REACTOME_TRAFFICKING_OF_AMPA_RECEPTORS
## 307 REACTOME_DOWNREGULATION_OF_ERBB2_SIGNALING
## 654 REACTOME_METALLOPROTEASE_DUBS
## 1234 REACTOME_TLR3_MEDIATED_TICAM1_DEPENDENT_PROGRAMMED_CELL_DEATH
## 1300 REACTOME_TRIF_MEDIATED_PROGRAMMED_CELL_DEATH
## 490 REACTOME_HDR_THROUGH_SINGLE_STRAND_ANNEALING_SSA
## 503 REACTOME_HOMOLOGOUS_DNA_PAIRING_AND_STRAND_EXCHANGE
## 1217 REACTOME_TFAP2_AP_2_FAMILY_REGULATES_TRANSCRIPTION_OF_GROWTH_FACTORS_AND_THEIR_RECEPTORS
## 235 REACTOME_CYTOSOLIC_IRON_SULFUR_CLUSTER_ASSEMBLY
## 775 REACTOME_O_GLYCOSYLATION_OF_TSR_DOMAIN_CONTAINING_PROTEINS
## 812 REACTOME_PHOSPHATE_BOND_HYDROLYSIS_BY_NUDT_PROTEINS
## 209 REACTOME_CONSTITUTIVE_SIGNALING_BY_LIGAND_RESPONSIVE_EGFR_CANCER_VARIANTS
## 454 REACTOME_GLUCOSE_METABOLISM
## 1071 REACTOME_SIGNALING_BY_EGFR_IN_CANCER
## 869 REACTOME_PROCESSIVE_SYNTHESIS_ON_THE_LAGGING_STRAND
## 1141 REACTOME_SODIUM_COUPLED_PHOSPHATE_COTRANSPORTERS
## 492 REACTOME_HEDGEHOG_OFF_STATE
## 858 REACTOME_PRE_NOTCH_PROCESSING_IN_THE_ENDOPLASMIC_RETICULUM
## 990 REACTOME_RNA_POLYMERASE_II_TRANSCRIBES_SNRNA_GENES
## 1105 REACTOME_SIGNALING_BY_NOTCH2
## 584 REACTOME_KERATAN_SULFATE_BIOSYNTHESIS
## 662 REACTOME_MISMATCH_REPAIR
## 1180 REACTOME_SYNTHESIS_OF_BILE_ACIDS_AND_BILE_SALTS_VIA_7ALPHA_HYDROXYCHOLESTEROL
## 1211 REACTOME_TELOMERE_C_STRAND_SYNTHESIS_INITIATION
## 62 REACTOME_ALPHA_PROTEIN_KINASE_1_SIGNALING_PATHWAY
## 575 REACTOME_IRAK2_MEDIATED_ACTIVATION_OF_TAK1_COMPLEX
## 1231 REACTOME_TICAM1_TRAF6_DEPENDENT_INDUCTION_OF_TAK1_COMPLEX
## 1252 REACTOME_TRAF6_MEDIATED_INDUCTION_OF_TAK1_COMPLEX_WITHIN_TLR4_COMPLEX
## 146 REACTOME_CASPASE_ACTIVATION_VIA_DEATH_RECEPTORS_IN_THE_PRESENCE_OF_LIGAND
## 246 REACTOME_DEADENYLATION_OF_MRNA
## 463 REACTOME_GLYCOGEN_METABOLISM
## 916 REACTOME_REGULATION_BY_C_FLIP
## 501 REACTOME_HIV_TRANSCRIPTION_ELONGATION
## 51 REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS
## 964 REACTOME_RESPONSE_OF_EIF2AK1_HRI_TO_HEME_DEFICIENCY
## 262 REACTOME_DEFECTIVE_LFNG_CAUSES_SCDO3
## 164 REACTOME_CELL_EXTRACELLULAR_MATRIX_INTERACTIONS
## 1168 REACTOME_SURFACTANT_METABOLISM
## 1246 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_DNA_REPAIR_GENES
## 923 REACTOME_REGULATION_OF_FZD_BY_UBIQUITINATION
## 387 REACTOME_FGFR2_ALTERNATIVE_SPLICING
## 155 REACTOME_CD28_DEPENDENT_PI3K_AKT_SIGNALING
## 216 REACTOME_COPII_MEDIATED_VESICLE_TRANSPORT
## 356 REACTOME_ESTABLISHMENT_OF_SISTER_CHROMATID_COHESION
## 85 REACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS
## 207 REACTOME_CONSTITUTIVE_SIGNALING_BY_AKT1_E17K_IN_CANCER
## 1263 REACTOME_TRANSCRIPTION_OF_E2F_TARGETS_UNDER_NEGATIVE_CONTROL_BY_P107_RBL1_AND_P130_RBL2_IN_COMPLEX_WITH_HDAC1
## 220 REACTOME_CREB_PHOSPHORYLATION
## 1237 REACTOME_TNFR1_INDUCED_PROAPOPTOTIC_SIGNALING
## 755 REACTOME_NOTCH4_INTRACELLULAR_DOMAIN_REGULATES_TRANSCRIPTION
## 723 REACTOME_NEPHRIN_FAMILY_INTERACTIONS
## 1049 REACTOME_SENSING_OF_DNA_DOUBLE_STRAND_BREAKS
## 1328 REACTOME_VIRAL_MESSENGER_RNA_SYNTHESIS
## 951 REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_ASSOCIATION_WITH_CO_FACTORS
## 1229 REACTOME_TICAM1_DEPENDENT_ACTIVATION_OF_IRF3_IRF7
## 634 REACTOME_MET_PROMOTES_CELL_MOTILITY
## 879 REACTOME_PROTON_COUPLED_MONOCARBOXYLATE_TRANSPORT
## 600 REACTOME_LISTERIA_MONOCYTOGENES_ENTRY_INTO_HOST_CELLS
## 876 REACTOME_PROTEIN_PROTEIN_INTERACTIONS_AT_SYNAPSES
## 183 REACTOME_CHYLOMICRON_CLEARANCE
## 527 REACTOME_INLB_MEDIATED_ENTRY_OF_LISTERIA_MONOCYTOGENES_INTO_HOST_CELL
## 1273 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_THE_AP_2_TFAP2_FAMILY_OF_TRANSCRIPTION_FACTORS
## 462 REACTOME_GLYCOGEN_BREAKDOWN_GLYCOGENOLYSIS
## 725 REACTOME_NETRIN_1_SIGNALING
## 193 REACTOME_CLEC7A_INFLAMMASOME_PATHWAY
## 400 REACTOME_FORMATION_OF_INCISION_COMPLEX_IN_GG_NER
## 1148 REACTOME_STRIATED_MUSCLE_CONTRACTION
## 28 REACTOME_ACTIVATION_OF_C3_AND_C5
## 352 REACTOME_ERYTHROPOIETIN_ACTIVATES_PHOSPHOLIPASE_C_GAMMA_PLCG
## 750 REACTOME_NOTCH2_ACTIVATION_AND_TRANSMISSION_OF_SIGNAL_TO_THE_NUCLEUS
## 1034 REACTOME_SCAVENGING_BY_CLASS_A_RECEPTORS
## 499 REACTOME_HIV_INFECTION
## 1226 REACTOME_THROMBIN_SIGNALLING_THROUGH_PROTEINASE_ACTIVATED_RECEPTORS_PARS
## 590 REACTOME_KINESINS
## 1102 REACTOME_SIGNALING_BY_NOTCH1_HD_DOMAIN_MUTANTS_IN_CANCER
## 588 REACTOME_KETONE_BODY_METABOLISM
## 153 REACTOME_CD22_MEDIATED_BCR_REGULATION
## 159 REACTOME_CELL_CELL_JUNCTION_ORGANIZATION
## 489 REACTOME_HDR_THROUGH_MMEJ_ALT_NHEJ
## 622 REACTOME_MECP2_REGULATES_TRANSCRIPTION_FACTORS
## 628 REACTOME_MET_ACTIVATES_PI3K_AKT_SIGNALING
## 635 REACTOME_MET_RECEPTOR_ACTIVATION
## 756 REACTOME_NR1H2_AND_NR1H3_MEDIATED_SIGNALING
## 840 REACTOME_PLATELET_CALCIUM_HOMEOSTASIS
## 893 REACTOME_RA_BIOSYNTHESIS_PATHWAY
## 913 REACTOME_REDUCTION_OF_CYTOSOLIC_CA_LEVELS
## 1115 REACTOME_SIGNALING_BY_PTK6
## 1167 REACTOME_SUPPRESSION_OF_PHAGOSOMAL_MATURATION
## 1047 REACTOME_SEMAPHORIN_INTERACTIONS
## 748 REACTOME_NOTCH_HLH_TRANSCRIPTION_PATHWAY
## 230 REACTOME_CTLA4_INHIBITORY_SIGNALING
## 802 REACTOME_PEPTIDE_HORMONE_BIOSYNTHESIS
## 103 REACTOME_ATTACHMENT_OF_GPI_ANCHOR_TO_UPAR
## 1161 REACTOME_SUMOYLATION_OF_RNA_BINDING_PROTEINS
## 1346 REACTOME_ZINC_EFFLUX_AND_COMPARTMENTALIZATION_BY_THE_SLC30_FAMILY
## 282 REACTOME_DIGESTION_OF_DIETARY_CARBOHYDRATE
## 104 REACTOME_ATTENUATION_PHASE
## 810 REACTOME_PHENYLALANINE_AND_TYROSINE_METABOLISM
## 1309 REACTOME_TRNA_PROCESSING_IN_THE_NUCLEUS
## 1297 REACTOME_TRANSPORT_OF_THE_SLBP_DEPENDANT_MATURE_MRNA
## 543 REACTOME_INTERFERON_SIGNALING
## 496 REACTOME_HEPARAN_SULFATE_HEPARIN_HS_GAG_METABOLISM
## 712 REACTOME_NEGATIVE_REGULATION_OF_FGFR1_SIGNALING
## 713 REACTOME_NEGATIVE_REGULATION_OF_FGFR2_SIGNALING
## 714 REACTOME_NEGATIVE_REGULATION_OF_FGFR3_SIGNALING
## 715 REACTOME_NEGATIVE_REGULATION_OF_FGFR4_SIGNALING
## 1143 REACTOME_SPRY_REGULATION_OF_FGF_SIGNALING
## 288 REACTOME_DISEASES_OF_CARBOHYDRATE_METABOLISM
## 377 REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION
## 1029 REACTOME_RUNX3_REGULATES_WNT_SIGNALING
## 1044 REACTOME_SEMA4D_IN_SEMAPHORIN_SIGNALING
## 140 REACTOME_CALNEXIN_CALRETICULIN_CYCLE
## 1072 REACTOME_SIGNALING_BY_ERBB2
## 507 REACTOME_HS_GAG_DEGRADATION
## 77 REACTOME_APC_C_CDC20_MEDIATED_DEGRADATION_OF_CYCLIN_B
## 453 REACTOME_GLUCONEOGENESIS
## 910 REACTOME_RECYCLING_OF_BILE_ACIDS_AND_SALTS
## 1296 REACTOME_TRANSPORT_OF_ORGANIC_ANIONS
## 1241 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_ADDITIONAL_CELL_CYCLE_GENES_WHOSE_EXACT_ROLE_IN_THE_P53_PATHWAY_REMAIN_UNCERTAIN
## 217 REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY
## 75 REACTOME_ANTIMICROBIAL_PEPTIDES
## 516 REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL
## 382 REACTOME_FERTILIZATION
## 538 REACTOME_INTERACTION_WITH_CUMULUS_CELLS_AND_THE_ZONA_PELLUCIDA
## 720 REACTOME_NEGATIVE_REGULATION_OF_TCF_DEPENDENT_SIGNALING_BY_DVL_INTERACTING_PROTEINS
## 1028 REACTOME_RUNX3_REGULATES_P14_ARF
## 988 REACTOME_RNA_POLYMERASE_I_TRANSCRIPTION_INITIATION
## 961 REACTOME_RESOLUTION_OF_D_LOOP_STRUCTURES
## 1262 REACTOME_TRANSCRIPTION_OF_E2F_TARGETS_UNDER_NEGATIVE_CONTROL_BY_DREAM_COMPLEX
## 1312 REACTOME_TYPE_I_HEMIDESMOSOME_ASSEMBLY
## 302 REACTOME_DNA_REPLICATION_INITIATION
## 318 REACTOME_DUAL_INCISION_IN_GG_NER
## 815 REACTOME_PHOSPHOLIPID_METABOLISM
## 1078 REACTOME_SIGNALING_BY_FGFR_IN_DISEASE
## 653 REACTOME_METAL_SEQUESTRATION_BY_ANTIMICROBIAL_PROTEINS
## 148 REACTOME_CASPASE_ACTIVATION_VIA_EXTRINSIC_APOPTOTIC_SIGNALLING_PATHWAY
## 699 REACTOME_NADE_MODULATES_DEATH_SIGNALLING
## 219 REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS
## 783 REACTOME_ORGANIC_ANION_TRANSPORT
## 784 REACTOME_ORGANIC_CATION_ANION_ZWITTERION_TRANSPORT
## 506 REACTOME_HS_GAG_BIOSYNTHESIS
## 296 REACTOME_DNA_DAMAGE_BYPASS
## 438 REACTOME_GALACTOSE_CATABOLISM
## 1236 REACTOME_TNFR1_INDUCED_NFKAPPAB_SIGNALING_PATHWAY
## 449 REACTOME_GLOBAL_GENOME_NUCLEOTIDE_EXCISION_REPAIR_GG_NER
## 533 REACTOME_INTEGRATION_OF_ENERGY_METABOLISM
## 950 REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_ACETYLATION
## 1220 REACTOME_TGF_BETA_RECEPTOR_SIGNALING_IN_EMT_EPITHELIAL_TO_MESENCHYMAL_TRANSITION
## 753 REACTOME_NOTCH3_INTRACELLULAR_DOMAIN_REGULATES_TRANSCRIPTION
## 1196 REACTOME_SYNTHESIS_OF_PIPS_AT_THE_LATE_ENDOSOME_MEMBRANE
## 786 REACTOME_OTHER_SEMAPHORIN_INTERACTIONS
## 782 REACTOME_ORGANELLE_BIOGENESIS_AND_MAINTENANCE
## 661 REACTOME_MISCELLANEOUS_TRANSPORT_AND_BINDING_EVENTS
## 1007 REACTOME_RRNA_PROCESSING_IN_THE_MITOCHONDRION
## 1280 REACTOME_TRANSLATION_OF_REPLICASE_AND_ASSEMBLY_OF_THE_REPLICATION_TRANSCRIPTION_COMPLEX
## 76 REACTOME_ANTIVIRAL_MECHANISM_BY_IFN_STIMULATED_GENES
## 665 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION
## 683 REACTOME_MRNA_CAPPING
## 1295 REACTOME_TRANSPORT_OF_NUCLEOTIDE_SUGARS
## 440 REACTOME_GAMMA_CARBOXYLATION_TRANSPORT_AND_AMINO_TERMINAL_CLEAVAGE_OF_PROTEINS
## 1248 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_G1_CELL_CYCLE_ARREST
## 607 REACTOME_LYSINE_CATABOLISM
## 761 REACTOME_NRCAM_INTERACTIONS
## 693 REACTOME_MYOCLONIC_EPILEPSY_OF_LAFORA
## 1063 REACTOME_SIGNAL_REGULATORY_PROTEIN_FAMILY_INTERACTIONS
## 940 REACTOME_REGULATION_OF_PTEN_LOCALIZATION
## 765 REACTOME_NUCLEAR_ENVELOPE_BREAKDOWN
## 981 REACTOME_RHO_GTPASES_ACTIVATE_RHOTEKIN_AND_RHOPHILINS
## 142 REACTOME_CARGO_CONCENTRATION_IN_THE_ER
## 373 REACTOME_FATTY_ACYL_COA_BIOSYNTHESIS
## 552 REACTOME_INTERLEUKIN_2_SIGNALING
## 776 REACTOME_O_LINKED_GLYCOSYLATION
## 1166 REACTOME_SUPPRESSION_OF_APOPTOSIS
## 716 REACTOME_NEGATIVE_REGULATION_OF_MAPK_PATHWAY
## 371 REACTOME_FATTY_ACID_METABOLISM
## 34 REACTOME_ACTIVATION_OF_NIMA_KINASES_NEK9_NEK6_NEK7
## 90 REACTOME_ARMS_MEDIATED_ACTIVATION
## 778 REACTOME_OAS_ANTIVIRAL_RESPONSE
## 898 REACTOME_RAF_INDEPENDENT_MAPK1_3_ACTIVATION
## 979 REACTOME_RHO_GTPASES_ACTIVATE_PAKS
## 267 REACTOME_DEFECTS_OF_CONTACT_ACTIVATION_SYSTEM_CAS_AND_KALLIKREIN_KININ_SYSTEM_KKS
## 759 REACTOME_NR1H3_NR1H2_REGULATE_GENE_EXPRESSION_LINKED_TO_CHOLESTEROL_TRANSPORT_AND_EFFLUX
## 803 REACTOME_PEPTIDE_HORMONE_METABOLISM
## 1109 REACTOME_SIGNALING_BY_NTRK3_TRKC
## 1214 REACTOME_TERMINAL_PATHWAY_OF_COMPLEMENT
## 757 REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_LINKED_TO_LIPOGENESIS
## 576 REACTOME_IRE1ALPHA_ACTIVATES_CHAPERONES
## 73 REACTOME_ANTIGEN_PRESENTATION_FOLDING_ASSEMBLY_AND_PEPTIDE_LOADING_OF_CLASS_I_MHC
## 369 REACTOME_FANCONI_ANEMIA_PATHWAY
## 147 REACTOME_CASPASE_ACTIVATION_VIA_DEPENDENCE_RECEPTORS_IN_THE_ABSENCE_OF_LIGAND
## 823 REACTOME_PI_METABOLISM
## 266 REACTOME_DEFECTS_IN_VITAMIN_AND_COFACTOR_METABOLISM
## 31 REACTOME_ACTIVATION_OF_IRF3_IRF7_MEDIATED_BY_TBK1_IKK_EPSILON
## 2 REACTOME_A_TETRASACCHARIDE_LINKER_SEQUENCE_IS_REQUIRED_FOR_GAG_SYNTHESIS
## 466 REACTOME_GLYCOLYSIS
## 1126 REACTOME_SIGNALING_BY_VEGF
## 27 REACTOME_ACTIVATION_OF_BH3_ONLY_PROTEINS
## 261 REACTOME_DEFECTIVE_FACTOR_VIII_CAUSES_HEMOPHILIA_A
## 768 REACTOME_NUCLEAR_IMPORT_OF_REV_PROTEIN
## 850 REACTOME_POSTMITOTIC_NUCLEAR_PORE_COMPLEX_NPC_REFORMATION
## 1040 REACTOME_SELECTIVE_AUTOPHAGY
## 1283 REACTOME_TRANSLESION_SYNTHESIS_BY_POLK
## 459 REACTOME_GLUTATHIONE_SYNTHESIS_AND_RECYCLING
## 615 REACTOME_MAPK3_ERK1_ACTIVATION
## 1157 REACTOME_SUMOYLATION_OF_DNA_METHYLATION_PROTEINS
## 925 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_LATE_STAGE_BRANCHING_MORPHOGENESIS_PANCREATIC_BUD_PRECURSOR_CELLS
## 1027 REACTOME_RUNX3_REGULATES_NOTCH_SIGNALING
## 721 REACTOME_NEGATIVE_REGULATION_OF_TCF_DEPENDENT_SIGNALING_BY_WNT_LIGAND_ANTAGONISTS
## 452 REACTOME_GLUCAGON_TYPE_LIGAND_RECEPTORS
## 873 REACTOME_PROSTACYCLIN_SIGNALLING_THROUGH_PROSTACYCLIN_RECEPTOR
## 1207 REACTOME_TBC_RABGAPS
## 897 REACTOME_RAF_ACTIVATION
## 84 REACTOME_APOPTOTIC_CLEAVAGE_OF_CELL_ADHESION_PROTEINS
## 245 REACTOME_DEADENYLATION_DEPENDENT_MRNA_DECAY
## 1079 REACTOME_SIGNALING_BY_FGFR1
## 1084 REACTOME_SIGNALING_BY_FGFR3
## 1086 REACTOME_SIGNALING_BY_FGFR4
## 880 REACTOME_PTEN_REGULATION
## 283 REACTOME_DISASSEMBLY_OF_THE_DESTRUCTION_COMPLEX_AND_RECRUITMENT_OF_AXIN_TO_THE_MEMBRANE
## 258 REACTOME_DEFECTIVE_EXT2_CAUSES_EXOSTOSES_2
## 882 REACTOME_PTK6_PROMOTES_HIF1A_STABILIZATION
## 793 REACTOME_P75NTR_RECRUITS_SIGNALLING_COMPLEXES
## 461 REACTOME_GLYCEROPHOSPHOLIPID_CATABOLISM
## 1181 REACTOME_SYNTHESIS_OF_DIPHTHAMIDE_EEF2
## 291 REACTOME_DISEASES_OF_MISMATCH_REPAIR_MMR
## 464 REACTOME_GLYCOGEN_STORAGE_DISEASES
## 852 REACTOME_POU5F1_OCT4_SOX2_NANOG_ACTIVATE_GENES_RELATED_TO_PROLIFERATION
## 549 REACTOME_INTERLEUKIN_17_SIGNALING
## 310 REACTOME_DOWNREGULATION_OF_TGF_BETA_RECEPTOR_SIGNALING
## 106 REACTOME_AURKA_ACTIVATION_BY_TPX2
## 280 REACTOME_DIGESTION
## 281 REACTOME_DIGESTION_AND_ABSORPTION
## 579 REACTOME_IRON_UPTAKE_AND_TRANSPORT
## 644 REACTOME_METABOLISM_OF_INGESTED_SEMET_SEC_MESEC_INTO_H2SE
## 807 REACTOME_PEXOPHAGY
## 1099 REACTOME_SIGNALING_BY_NODAL
## 1288 REACTOME_TRANSPORT_OF_BILE_SALTS_AND_ORGANIC_ACIDS_METAL_IONS_AND_AMINE_COMPOUNDS
## 636 REACTOME_MET_RECEPTOR_RECYCLING
## 948 REACTOME_REGULATION_OF_SIGNALING_BY_CBL
## 478 REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING
## 334 REACTOME_ENERGY_DEPENDENT_REGULATION_OF_MTOR_BY_LKB1_AMPK
## 497 REACTOME_HISTIDINE_CATABOLISM
## 956 REACTOME_REMOVAL_OF_AMINOTERMINAL_PROPEPTIDES_FROM_GAMMA_CARBOXYLATED_PROTEINS
## 1320 REACTOME_UPTAKE_AND_ACTIONS_OF_BACTERIAL_TOXINS
## 66 REACTOME_AMINO_ACIDS_REGULATE_MTORC1
## 541 REACTOME_INTERCONVERSION_OF_NUCLEOTIDE_DI_AND_TRIPHOSPHATES
## 630 REACTOME_MET_ACTIVATES_PTPN11
## 1290 REACTOME_TRANSPORT_OF_FATTY_ACIDS
## 770 REACTOME_NUCLEAR_RECEPTOR_TRANSCRIPTION_PATHWAY
## 696 REACTOME_N_GLYCAN_ANTENNAE_ELONGATION_IN_THE_MEDIAL_TRANS_GOLGI
## 1022 REACTOME_RUNX2_REGULATES_GENES_INVOLVED_IN_CELL_MIGRATION
## 652 REACTOME_METAL_ION_SLC_TRANSPORTERS
## 908 REACTOME_RECRUITMENT_OF_MITOTIC_CENTROSOME_PROTEINS_AND_COMPLEXES
## 938 REACTOME_REGULATION_OF_PLK1_ACTIVITY_AT_G2_M_TRANSITION
## 1140 REACTOME_SODIUM_CALCIUM_EXCHANGERS
## 61 REACTOME_ALPHA_LINOLENIC_OMEGA3_AND_LINOLEIC_OMEGA6_ACID_METABOLISM
## 1343 REACTOME_WNT5A_DEPENDENT_INTERNALIZATION_OF_FZD4
## 121 REACTOME_BETA_OXIDATION_OF_VERY_LONG_CHAIN_FATTY_ACIDS
## 731 REACTOME_NEUROTRANSMITTER_CLEARANCE
## 1270 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_RUNX2
## 326 REACTOME_EGFR_DOWNREGULATION
## 488 REACTOME_HDR_THROUGH_HOMOLOGOUS_RECOMBINATION_HRR
## 413 REACTOME_FOXO_MEDIATED_TRANSCRIPTION_OF_OXIDATIVE_STRESS_METABOLIC_AND_NEURONAL_GENES
## 970 REACTOME_RETINOID_CYCLE_DISEASE_EVENTS
## 308 REACTOME_DOWNREGULATION_OF_ERBB4_SIGNALING
## 265 REACTOME_DEFECTS_IN_COBALAMIN_B12_METABOLISM
## 188 REACTOME_CLASS_B_2_SECRETIN_FAMILY_RECEPTORS
## 1221 REACTOME_THE_ACTIVATION_OF_ARYLSULFATASES
## 769 REACTOME_NUCLEAR_PORE_COMPLEX_NPC_DISASSEMBLY
## 222 REACTOME_CREB1_PHOSPHORYLATION_THROUGH_THE_ACTIVATION_OF_ADENYLATE_CYCLASE
## 1093 REACTOME_SIGNALING_BY_LRP5_MUTANTS
## 1190 REACTOME_SYNTHESIS_OF_PE
## 1138 REACTOME_SMOOTH_MUSCLE_CONTRACTION
## 441 REACTOME_GAP_FILLING_DNA_REPAIR_SYNTHESIS_AND_LIGATION_IN_GG_NER
## 839 REACTOME_PLATELET_AGGREGATION_PLUG_FORMATION
## 3 REACTOME_ABACAVIR_METABOLISM
## 4 REACTOME_ABACAVIR_TRANSPORT_AND_METABOLISM
## 1175 REACTOME_SYNTHESIS_OF_5_EICOSATETRAENOIC_ACIDS
## 32 REACTOME_ACTIVATION_OF_KAINATE_RECEPTORS_UPON_GLUTAMATE_BINDING
## 525 REACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION
## 1326 REACTOME_VEGFR2_MEDIATED_VASCULAR_PERMEABILITY
## 394 REACTOME_FGFRL1_MODULATION_OF_FGFR1_SIGNALING
## 57 REACTOME_AFLATOXIN_ACTIVATION_AND_DETOXIFICATION
## 476 REACTOME_GRB2_SOS_PROVIDES_LINKAGE_TO_MAPK_SIGNALING_FOR_INTEGRINS
## 722 REACTOME_NEGATIVE_REGULATORS_OF_DDX58_IFIH1_SIGNALING
## 1149 REACTOME_SULFIDE_OXIDATION_TO_SULFATE
## 695 REACTOME_N_GLYCAN_ANTENNAE_ELONGATION
## 276 REACTOME_DERMATAN_SULFATE_BIOSYNTHESIS
## 1187 REACTOME_SYNTHESIS_OF_LIPOXINS_LX
## 1194 REACTOME_SYNTHESIS_OF_PIPS_AT_THE_ER_MEMBRANE
## 1347 REACTOME_ZINC_INFLUX_INTO_CELLS_BY_THE_SLC39_GENE_FAMILY
## 1184 REACTOME_SYNTHESIS_OF_IP3_AND_IP4_IN_THE_CYTOSOL
## 643 REACTOME_METABOLISM_OF_FOLATE_AND_PTERINES
## 785 REACTOME_OTHER_INTERLEUKIN_SIGNALING
## 166 REACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL
## 370 REACTOME_FASL_CD95L_SIGNALING
## 613 REACTOME_MAPK_TARGETS_NUCLEAR_EVENTS_MEDIATED_BY_MAP_KINASES
## 599 REACTOME_LIPOPHAGY
## 1188 REACTOME_SYNTHESIS_OF_PA
## 26 REACTOME_ACTIVATION_OF_BAD_AND_TRANSLOCATION_TO_MITOCHONDRIA
## 468 REACTOME_GLYCOSPHINGOLIPID_METABOLISM
## 1249 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_G2_CELL_CYCLE_ARREST
## 295 REACTOME_DISSOLUTION_OF_FIBRIN_CLOT
## 396 REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE
## 597 REACTOME_LECTIN_PATHWAY_OF_COMPLEMENT_ACTIVATION
## 616 REACTOME_MAPK6_MAPK4_SIGNALING
## 883 REACTOME_PTK6_REGULATES_CELL_CYCLE
## 354 REACTOME_ERYTHROPOIETIN_ACTIVATES_STAT5
## 367 REACTOME_EXTRINSIC_PATHWAY_OF_FIBRIN_CLOT_FORMATION
## 1336 REACTOME_VLDLR_INTERNALISATION_AND_DEGRADATION
## 10 REACTOME_ABORTIVE_ELONGATION_OF_HIV_1_TRANSCRIPT_IN_THE_ABSENCE_OF_TAT
## 80 REACTOME_APC_CDC20_MEDIATED_DEGRADATION_OF_NEK2A
## 1094 REACTOME_SIGNALING_BY_MAPK_MUTANTS
## 251 REACTOME_DEFECTIVE_B4GALT7_CAUSES_EDS_PROGEROID_TYPE
## 471 REACTOME_GOLGI_CISTERNAE_PERICENTRIOLAR_STACK_REORGANIZATION
## 767 REACTOME_NUCLEAR_EVENTS_KINASE_AND_TRANSCRIPTION_FACTOR_ACTIVATION
## 37 REACTOME_ACTIVATION_OF_RAC1
## 69 REACTOME_ANCHORING_OF_THE_BASAL_BODY_TO_THE_PLASMA_MEMBRANE
## 9 REACTOME_ABERRANT_REGULATION_OF_MITOTIC_G1_S_TRANSITION_IN_CANCER_DUE_TO_RB1_DEFECTS
## 82 REACTOME_APOBEC3G_MEDIATED_RESISTANCE_TO_HIV_1_INFECTION
## 531 REACTOME_INSULIN_RECEPTOR_RECYCLING
## 49 REACTOME_ADENYLATE_CYCLASE_ACTIVATING_PATHWAY
## 297 REACTOME_DNA_DAMAGE_RECOGNITION_IN_GG_NER
## 972 REACTOME_RETROGRADE_TRANSPORT_AT_THE_TRANS_GOLGI_NETWORK
## 70 REACTOME_ANDROGEN_BIOSYNTHESIS
## 1265 REACTOME_TRANSCRIPTIONAL_ACTIVATION_OF_MITOCHONDRIAL_BIOGENESIS
## 279 REACTOME_DEX_H_BOX_HELICASES_ACTIVATE_TYPE_I_IFN_AND_INFLAMMATORY_CYTOKINES_PRODUCTION
## 619 REACTOME_MATURATION_OF_PROTEIN_3A
## 698 REACTOME_N_GLYCAN_TRIMMING_IN_THE_ER_AND_CALNEXIN_CALRETICULIN_CYCLE
## 363 REACTOME_EXPORT_OF_VIRAL_RIBONUCLEOPROTEINS_FROM_NUCLEUS
## 983 REACTOME_RHO_GTPASES_ACTIVATE_WASPS_AND_WAVES
## 421 REACTOME_G_ALPHA_12_13_SIGNALLING_EVENTS
## 828 REACTOME_PIWI_INTERACTING_RNA_PIRNA_BIOGENESIS
## 1129 REACTOME_SIGNALLING_TO_ERKS
## 46 REACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG
## 573 REACTOME_IONOTROPIC_ACTIVITY_OF_KAINATE_RECEPTORS
## 772 REACTOME_NUCLEOBASE_BIOSYNTHESIS
## 92 REACTOME_ASPARTATE_AND_ASPARAGINE_METABOLISM
## 477 REACTOME_GRB7_EVENTS_IN_ERBB2_SIGNALING
## 88 REACTOME_ARACHIDONATE_PRODUCTION_FROM_DAG
## 63 REACTOME_ALTERNATIVE_COMPLEMENT_ACTIVATION
## 13 REACTOME_ACTIVATED_NOTCH1_TRANSMITS_SIGNAL_TO_THE_NUCLEUS
## 232 REACTOME_CYCLIN_A_CDK2_ASSOCIATED_EVENTS_AT_S_PHASE_ENTRY
## 685 REACTOME_MRNA_DECAY_BY_5_TO_3_EXORIBONUCLEASE
## 777 REACTOME_O_LINKED_GLYCOSYLATION_OF_MUCINS
## 1051 REACTOME_SERINE_BIOSYNTHESIS
## 1342 REACTOME_WNT5A_DEPENDENT_INTERNALIZATION_OF_FZD2_FZD5_AND_ROR2
## 690 REACTOME_MUCOPOLYSACCHARIDOSES
## 606 REACTOME_LTC4_CYSLTR_MEDIATED_IL4_PRODUCTION
## 1257 REACTOME_TRAFFICKING_OF_GLUR2_CONTAINING_AMPA_RECEPTORS
## 705 REACTOME_NEF_MEDIATED_CD4_DOWN_REGULATION
## 327 REACTOME_EGFR_INTERACTS_WITH_PHOSPHOLIPASE_C_GAMMA
## 888 REACTOME_PURINE_SALVAGE
## 1163 REACTOME_SUMOYLATION_OF_TRANSCRIPTION_COFACTORS
## 122 REACTOME_BH3_ONLY_PROTEINS_ASSOCIATE_WITH_AND_INACTIVATE_ANTI_APOPTOTIC_BCL_2_MEMBERS
## 154 REACTOME_CD28_CO_STIMULATION
## 915 REACTOME_REGULATED_PROTEOLYSIS_OF_P75NTR
## 1165 REACTOME_SUMOYLATION_OF_UBIQUITINYLATION_PROTEINS
## 1201 REACTOME_SYNTHESIS_OF_UDP_N_ACETYL_GLUCOSAMINE
## 1242 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_CASPASE_ACTIVATORS_AND_CASPASES
## 395 REACTOME_FIBRONECTIN_MATRIX_FORMATION
## 658 REACTOME_MHC_CLASS_II_ANTIGEN_PRESENTATION
## 1189 REACTOME_SYNTHESIS_OF_PC
## 277 REACTOME_DETOXIFICATION_OF_REACTIVE_OXYGEN_SPECIES
## 885 REACTOME_PTK6_REGULATES_RHO_GTPASES_RAS_GTPASE_AND_MAP_KINASES
## 1219 REACTOME_TGF_BETA_RECEPTOR_SIGNALING_ACTIVATES_SMADS
## 902 REACTOME_RAS_SIGNALING_DOWNSTREAM_OF_NF1_LOSS_OF_FUNCTION_VARIANTS
## 991 REACTOME_RNA_POLYMERASE_II_TRANSCRIPTION_TERMINATION
## 829 REACTOME_PKA_ACTIVATION_IN_GLUCAGON_SIGNALLING
## 872 REACTOME_PROPIONYL_COA_CATABOLISM
## 1125 REACTOME_SIGNALING_BY_TYPE_1_INSULIN_LIKE_GROWTH_FACTOR_1_RECEPTOR_IGF1R
## 1121 REACTOME_SIGNALING_BY_TGF_BETA_RECEPTOR_COMPLEX
## 259 REACTOME_DEFECTIVE_F9_ACTIVATION
## 542 REACTOME_INTERFERON_GAMMA_SIGNALING
## 907 REACTOME_RECOGNITION_OF_DNA_DAMAGE_BY_PCNA_CONTAINING_REPLICATION_COMPLEX
## 926 REACTOME_REGULATION_OF_GLUCOKINASE_BY_GLUCOKINASE_REGULATORY_PROTEIN
## 954 REACTOME_REGULATION_OF_TP53_EXPRESSION_AND_DEGRADATION
## 398 REACTOME_FORMATION_OF_ATP_BY_CHEMIOSMOTIC_COUPLING
## 749 REACTOME_NOTCH1_INTRACELLULAR_DOMAIN_REGULATES_TRANSCRIPTION
## 545 REACTOME_INTERLEUKIN_10_SIGNALING
## 659 REACTOME_MICRORNA_MIRNA_BIOGENESIS
## 101 REACTOME_ATF6_ATF6_ALPHA_ACTIVATES_CHAPERONE_GENES
## 102 REACTOME_ATF6_ATF6_ALPHA_ACTIVATES_CHAPERONES
## 532 REACTOME_INSULIN_RECEPTOR_SIGNALLING_CASCADE
## 1030 REACTOME_RUNX3_REGULATES_YAP1_MEDIATED_TRANSCRIPTION
## 249 REACTOME_DECTIN_2_FAMILY
## 133 REACTOME_BUDDING_AND_MATURATION_OF_HIV_VIRION
## 577 REACTOME_IRF3_MEDIATED_ACTIVATION_OF_TYPE_1_IFN
## 919 REACTOME_REGULATION_OF_COMMISSURAL_AXON_PATHFINDING_BY_SLIT_AND_ROBO
## 1162 REACTOME_SUMOYLATION_OF_SUMOYLATION_PROTEINS
## 1200 REACTOME_SYNTHESIS_OF_SUBSTRATES_IN_N_GLYCAN_BIOSYTHESIS
## 1104 REACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_M1580_K2555_TRANSLOCATION_MUTANT
## 1314 REACTOME_TYSND1_CLEAVES_PEROXISOMAL_PROTEINS
## 1036 REACTOME_SCAVENGING_BY_CLASS_F_RECEPTORS
## 138 REACTOME_CA2_PATHWAY
## 1199 REACTOME_SYNTHESIS_OF_PYROPHOSPHATES_IN_THE_CYTOSOL
## 33 REACTOME_ACTIVATION_OF_MATRIX_METALLOPROTEINASES
## 412 REACTOME_FOXO_MEDIATED_TRANSCRIPTION_OF_CELL_DEATH_GENES
## 12 REACTOME_ACETYLCHOLINE_REGULATES_INSULIN_SECRETION
## 623 REACTOME_MECP2_REGULATES_TRANSCRIPTION_OF_NEURONAL_LIGANDS
## 866 REACTOME_PROCESSING_OF_INTRONLESS_PRE_MRNAS
## 892 REACTOME_PYRUVATE_METABOLISM_AND_CITRIC_ACID_TCA_CYCLE
## 325 REACTOME_EFFECTS_OF_PIP2_HYDROLYSIS
## 806 REACTOME_PEROXISOMAL_PROTEIN_IMPORT
## 191 REACTOME_CLEC7A_DECTIN_1_INDUCES_NFAT_ACTIVATION
## 322 REACTOME_E3_UBIQUITIN_LIGASES_UBIQUITINATE_TARGET_PROTEINS
## 241 REACTOME_DAP12_SIGNALING
## 881 REACTOME_PTK6_EXPRESSION
## 522 REACTOME_INHIBITION_OF_REPLICATION_INITIATION_OF_DAMAGED_DNA_BY_RB1_E2F1
## 1003 REACTOME_ROS_AND_RNS_PRODUCTION_IN_PHAGOCYTES
## 409 REACTOME_FORMYL_PEPTIDE_RECEPTORS_BIND_FORMYL_PEPTIDES_AND_MANY_OTHER_LIGANDS
## 167 REACTOME_CELLULAR_HEXOSE_TRANSPORT
## 827 REACTOME_PINK1_PRKN_MEDIATED_MITOPHAGY
## 789 REACTOME_P130CAS_LINKAGE_TO_MAPK_SIGNALING_FOR_INTEGRINS
## 1177 REACTOME_SYNTHESIS_OF_BILE_ACIDS_AND_BILE_SALTS
## 470 REACTOME_GOLGI_ASSOCIATED_VESICLE_BIOGENESIS
## 639 REACTOME_METABOLISM_OF_ANGIOTENSINOGEN_TO_ANGIOTENSINS
## 825 REACTOME_PI3K_EVENTS_IN_ERBB2_SIGNALING
## 143 REACTOME_CARGO_RECOGNITION_FOR_CLATHRIN_MEDIATED_ENDOCYTOSIS
## 792 REACTOME_P75NTR_NEGATIVELY_REGULATES_CELL_CYCLE_VIA_SC1
## 997 REACTOME_ROBO_RECEPTORS_BIND_AKAP5
## 1000 REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS
## 1306 REACTOME_TRNA_MODIFICATION_IN_THE_NUCLEUS_AND_CYTOSOL
## 1318 REACTOME_UNFOLDED_PROTEIN_RESPONSE_UPR
## 1088 REACTOME_SIGNALING_BY_HEDGEHOG
## 411 REACTOME_FOXO_MEDIATED_TRANSCRIPTION_OF_CELL_CYCLE_GENES
## 551 REACTOME_INTERLEUKIN_2_FAMILY_SIGNALING
## 791 REACTOME_P75_NTR_RECEPTOR_MEDIATED_SIGNALLING
## 969 REACTOME_RET_SIGNALING
## 1250 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_SEVERAL_ADDITIONAL_CELL_DEATH_GENES_WHOSE_SPECIFIC_ROLES_IN_P53_DEPENDENT_APOPTOSIS_REMAIN_UNCERTAIN
## 434 REACTOME_GAB1_SIGNALOSOME
## 617 REACTOME_MASTL_FACILITATES_MITOTIC_PROGRESSION
## 163 REACTOME_CELL_DEATH_SIGNALLING_VIA_NRAGE_NRIF_AND_NADE
## 718 REACTOME_NEGATIVE_REGULATION_OF_NMDA_RECEPTOR_MEDIATED_NEURONAL_TRANSMISSION
## 694 REACTOME_MYOGENESIS
## 621 REACTOME_MECP2_REGULATES_NEURONAL_RECEPTORS_AND_CHANNELS
## 72 REACTOME_ANTIGEN_ACTIVATES_B_CELL_RECEPTOR_BCR_LEADING_TO_GENERATION_OF_SECOND_MESSENGERS
## 864 REACTOME_PROCESSING_OF_CAPPED_INTRONLESS_PRE_MRNA
## 114 REACTOME_BETA_CATENIN_PHOSPHORYLATION_CASCADE
## 596 REACTOME_LDL_CLEARANCE
## 1068 REACTOME_SIGNALING_BY_CTNNB1_PHOSPHO_SITE_MUTANTS
## 175 REACTOME_CHOLESTEROL_BIOSYNTHESIS
## 358 REACTOME_ESTROGEN_DEPENDENT_NUCLEAR_EVENTS_DOWNSTREAM_OF_ESR_MEMBRANE_SIGNALING
## 645 REACTOME_METABOLISM_OF_NITRIC_OXIDE_NOS3_ACTIVATION_AND_REGULATION
## 511 REACTOME_HUR_ELAVL1_BINDS_AND_STABILIZES_MRNA
## 900 REACTOME_RAS_ACTIVATION_UPON_CA2_INFLUX_THROUGH_NMDA_RECEPTOR
## 1218 REACTOME_TFAP2A_ACTS_AS_A_TRANSCRIPTIONAL_REPRESSOR_DURING_RETINOIC_ACID_INDUCED_CELL_DIFFERENTIATION
## 201 REACTOME_COMMON_PATHWAY_OF_FIBRIN_CLOT_FORMATION
## 736 REACTOME_NF_KB_IS_ACTIVATED_AND_SIGNALS_SURVIVAL
## 739 REACTOME_NICOTINATE_METABOLISM
## 795 REACTOME_P75NTR_SIGNALS_VIA_NF_KB
## 1341 REACTOME_WNT_MEDIATED_ACTIVATION_OF_DVL
## 107 REACTOME_AUTOPHAGY
## 305 REACTOME_DOPAMINE_NEUROTRANSMITTER_RELEASE_CYCLE
## 581 REACTOME_IRS_MEDIATED_SIGNALLING
## 601 REACTOME_LONG_TERM_POTENTIATION
## 536 REACTOME_INTEGRIN_SIGNALING
## 842 REACTOME_PLATELET_SENSITIZATION_BY_LDL
## 29 REACTOME_ACTIVATION_OF_CASPASES_THROUGH_APOPTOSOME_MEDIATED_CLEAVAGE
## 1135 REACTOME_SMAC_XIAP_REGULATED_APOPTOTIC_RESPONSE
## 120 REACTOME_BETA_OXIDATION_OF_PRISTANOYL_COA
## 1348 REACTOME_ZINC_TRANSPORTERS
## 309 REACTOME_DOWNREGULATION_OF_SMAD2_3_SMAD4_TRANSCRIPTIONAL_ACTIVITY
## 381 REACTOME_FCGR3A_MEDIATED_IL10_SYNTHESIS
## 1001 REACTOME_ROLE_OF_SECOND_MESSENGERS_IN_NETRIN_1_SIGNALING
## 838 REACTOME_PLATELET_ADHESION_TO_EXPOSED_COLLAGEN
## 971 REACTOME_RETROGRADE_NEUROTROPHIN_SIGNALLING
## 406 REACTOME_FORMATION_OF_THE_EARLY_ELONGATION_COMPLEX
## 333 REACTOME_ENDOSOMAL_VACUOLAR_PATHWAY
## 996 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_TERMINATION
## 1103 REACTOME_SIGNALING_BY_NOTCH1_PEST_DOMAIN_MUTANTS_IN_CANCER
## 335 REACTOME_ENOS_ACTIVATION
## 156 REACTOME_CD28_DEPENDENT_VAV1_PATHWAY
## 351 REACTOME_ERYTHROPOIETIN_ACTIVATES_PHOSPHOINOSITIDE_3_KINASE_PI3K
## 1002 REACTOME_RORA_ACTIVATES_GENE_EXPRESSION
## 605 REACTOME_LRR_FLII_INTERACTING_PROTEIN_1_LRRFIP1_ACTIVATES_TYPE_I_IFN_PRODUCTION
## 818 REACTOME_PHYSIOLOGICAL_FACTORS
## 36 REACTOME_ACTIVATION_OF_PPARGC1A_PGC_1ALPHA_BY_PHOSPHORYLATION
## 332 REACTOME_ENDOSOMAL_SORTING_COMPLEX_REQUIRED_FOR_TRANSPORT_ESCRT
## 380 REACTOME_FCGR_ACTIVATION
## 887 REACTOME_PURINE_RIBONUCLEOSIDE_MONOPHOSPHATE_BIOSYNTHESIS
## 1043 REACTOME_SEMA3A_PLEXIN_REPULSION_SIGNALING_BY_INHIBITING_INTEGRIN_ADHESION
## 1113 REACTOME_SIGNALING_BY_PDGFR_IN_DISEASE
## 345 REACTOME_ERBB2_REGULATES_CELL_MOTILITY
## 505 REACTOME_HOST_INTERACTIONS_OF_HIV_FACTORS
## 1233 REACTOME_TIGHT_JUNCTION_INTERACTIONS
## 317 REACTOME_DSCAM_INTERACTIONS
## 1123 REACTOME_SIGNALING_BY_TGFB_FAMILY_MEMBERS
## 614 REACTOME_MAPK1_ERK2_ACTIVATION
## 338 REACTOME_EPHA_MEDIATED_GROWTH_CONE_COLLAPSE
## 1064 REACTOME_SIGNAL_TRANSDUCTION_BY_L1
## 1338 REACTOME_VXPX_CARGO_TARGETING_TO_CILIUM
## 691 REACTOME_MULTIFUNCTIONAL_ANION_EXCHANGERS
## 292 REACTOME_DISINHIBITION_OF_SNARE_FORMATION
## 710 REACTOME_NEGATIVE_FEEDBACK_REGULATION_OF_MAPK_PATHWAY
## 150 REACTOME_CATION_COUPLED_CHLORIDE_COTRANSPORTERS
## 1294 REACTOME_TRANSPORT_OF_NUCLEOSIDES_AND_FREE_PURINE_AND_PYRIMIDINE_BASES_ACROSS_THE_PLASMA_MEMBRANE
## 1130 REACTOME_SIGNALLING_TO_RAS
## 1198 REACTOME_SYNTHESIS_OF_PROSTAGLANDINS_PG_AND_THROMBOXANES_TX
## 632 REACTOME_MET_ACTIVATES_RAS_SIGNALING
## 711 REACTOME_NEGATIVE_REGULATION_OF_ACTIVITY_OF_TFAP2_AP_2_FAMILY_TRANSCRIPTION_FACTORS
## 1266 REACTOME_TRANSCRIPTIONAL_ACTIVITY_OF_SMAD2_SMAD3_SMAD4_HETEROTRIMER
## 1172 REACTOME_SYNTHESIS_OF_12_EICOSATETRAENOIC_ACID_DERIVATIVES
## 1173 REACTOME_SYNTHESIS_OF_15_EICOSATETRAENOIC_ACID_DERIVATIVES
## 841 REACTOME_PLATELET_HOMEOSTASIS
## 871 REACTOME_PROLONGED_ERK_ACTIVATION_EVENTS
## 145 REACTOME_CARNITINE_METABOLISM
## 517 REACTOME_INACTIVATION_OF_CDC42_AND_RAC1
## 849 REACTOME_POST_TRANSLATIONAL_MODIFICATION_SYNTHESIS_OF_GPI_ANCHORED_PROTEINS
## 1087 REACTOME_SIGNALING_BY_FGFR4_IN_DISEASE
## 1122 REACTOME_SIGNALING_BY_TGF_BETA_RECEPTOR_COMPLEX_IN_CANCER
## 1332 REACTOME_VITAMIN_C_ASCORBATE_METABOLISM
## 139 REACTOME_CALCINEURIN_ACTIVATES_NFAT
## 631 REACTOME_MET_ACTIVATES_RAP1_AND_RAC1
## 1222 REACTOME_THE_CANONICAL_RETINOID_CYCLE_IN_RODS_TWILIGHT_VISION
## 957 REACTOME_REPRESSION_OF_WNT_TARGET_GENES
## 1042 REACTOME_SEMA3A_PAK_DEPENDENT_AXON_REPULSION
## 708 REACTOME_NEF_MEDIATES_DOWN_MODULATION_OF_CELL_SURFACE_RECEPTORS_BY_RECRUITING_THEM_TO_CLATHRIN_ADAPTERS
## 1020 REACTOME_RUNX2_REGULATES_BONE_DEVELOPMENT
## 1245 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_DEATH_RECEPTORS_AND_LIGANDS
## 1259 REACTOME_TRAIL_SIGNALING
## 111 REACTOME_BASIGIN_INTERACTIONS
## 126 REACTOME_BIOSYNTHESIS_OF_EPA_DERIVED_SPMS
## 443 REACTOME_GAP_JUNCTION_DEGRADATION
## 674 REACTOME_MITOTIC_G2_G2_M_PHASES
## 129 REACTOME_BIOTIN_TRANSPORT_AND_METABOLISM
## 195 REACTOME_COENZYME_A_BIOSYNTHESIS
## 264 REACTOME_DEFECTS_IN_BIOTIN_BTN_METABOLISM
## 1334 REACTOME_VLDL_ASSEMBLY
## 115 REACTOME_BETA_OXIDATION_OF_BUTANOYL_COA_TO_ACETYL_COA
## 234 REACTOME_CYTOCHROME_C_MEDIATED_APOPTOTIC_RESPONSE
## 152 REACTOME_CD209_DC_SIGN_SIGNALING
## 998 REACTOME_ROLE_OF_ABL_IN_ROBO_SLIT_SIGNALING
## 218 REACTOME_CREATINE_METABOLISM
## 790 REACTOME_P38MAPK_EVENTS
## 727 REACTOME_NEUREXINS_AND_NEUROLIGINS
## 131 REACTOME_BMAL1_CLOCK_NPAS2_ACTIVATES_CIRCADIAN_GENE_EXPRESSION
## 244 REACTOME_DEACTIVATION_OF_THE_BETA_CATENIN_TRANSACTIVATING_COMPLEX
## 329 REACTOME_EGR2_AND_SOX10_MEDIATED_INITIATION_OF_SCHWANN_CELL_MYELINATION
## 930 REACTOME_REGULATION_OF_IFNG_SIGNALING
## 1009 REACTOME_RUNX1_AND_FOXP3_CONTROL_THE_DEVELOPMENT_OF_REGULATORY_T_LYMPHOCYTES_TREGS
## 1011 REACTOME_RUNX1_REGULATES_ESTROGEN_RECEPTOR_MEDIATED_TRANSCRIPTION
## 1014 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_BCR_SIGNALING
## 1018 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_INTERLEUKIN_SIGNALING
## 1019 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_WNT_SIGNALING
## 1021 REACTOME_RUNX2_REGULATES_CHONDROCYTE_MATURATION
## 1345 REACTOME_ZBP1_DAI_MEDIATED_INDUCTION_OF_TYPE_I_IFNS
## 704 REACTOME_NEF_AND_SIGNAL_TRANSDUCTION
## 1065 REACTOME_SIGNALING_BY_ACTIVIN
## 1317 REACTOME_UNBLOCKING_OF_NMDA_RECEPTORS_GLUTAMATE_BINDING_AND_ACTIVATION
## 22 REACTOME_ACTIVATED_TAK1_MEDIATES_P38_MAPK_ACTIVATION
## 458 REACTOME_GLUTATHIONE_CONJUGATION
## 1033 REACTOME_SARS_COV_1_INFECTION
## 359 REACTOME_ESTROGEN_STIMULATED_SIGNALING_THROUGH_PRKCZ
## 1092 REACTOME_SIGNALING_BY_LEPTIN
## 843 REACTOME_POLB_DEPENDENT_LONG_PATCH_BASE_EXCISION_REPAIR
## 955 REACTOME_RELEASE_OF_HH_NP_FROM_THE_SECRETING_CELL
## 1225 REACTOME_THE_ROLE_OF_NEF_IN_HIV_1_REPLICATION_AND_DISEASE_PATHOGENESIS
## 1340 REACTOME_WNT_LIGAND_BIOGENESIS_AND_TRAFFICKING
## 1322 REACTOME_UPTAKE_AND_FUNCTION_OF_DIPHTHERIA_TOXIN
## 638 REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES
## 774 REACTOME_NUCLEOTIDE_SALVAGE
## 56 REACTOME_ADVANCED_GLYCOSYLATION_ENDPRODUCT_RECEPTOR_SIGNALING
## 270 REACTOME_DEGRADATION_OF_CYSTEINE_AND_HOMOCYSTEINE
## 730 REACTOME_NEUROTOXICITY_OF_CLOSTRIDIUM_TOXINS
## 737 REACTOME_NGF_STIMULATED_TRANSCRIPTION
## 1136 REACTOME_SMAD2_SMAD3_SMAD4_HETEROTRIMER_REGULATES_TRANSCRIPTION
## 1240 REACTOME_TOXICITY_OF_BOTULINUM_TOXIN_TYPE_D_BOTD
## 243 REACTOME_DCC_MEDIATED_ATTRACTIVE_SIGNALING
## 375 REACTOME_FC_EPSILON_RECEPTOR_FCERI_SIGNALING
## 664 REACTOME_MITOCHONDRIAL_CALCIUM_ION_TRANSPORT
## 935 REACTOME_REGULATION_OF_LOCALIZATION_OF_FOXO_TRANSCRIPTION_FACTORS
## 975 REACTOME_RHO_GTPASES_ACTIVATE_FORMINS
## 1023 REACTOME_RUNX2_REGULATES_OSTEOBLAST_DIFFERENTIATION
## 591 REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA
## 984 REACTOME_RIP_MEDIATED_NFKB_ACTIVATION_VIA_ZBP1
## 728 REACTOME_NEUROFASCIN_INTERACTIONS
## 67 REACTOME_AMPK_INHIBITS_CHREBP_TRANSCRIPTIONAL_ACTIVATION_ACTIVITY
## 15 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FRS2_AND_FRS3
## 835 REACTOME_PLASMA_LIPOPROTEIN_CLEARANCE
## 30 REACTOME_ACTIVATION_OF_GENE_EXPRESSION_BY_SREBF_SREBP
## 224 REACTOME_CRISTAE_FORMATION
## 672 REACTOME_MITOPHAGY
## 1331 REACTOME_VITAMIN_B5_PANTOTHENATE_METABOLISM
## 754 REACTOME_NOTCH4_ACTIVATION_AND_TRANSMISSION_OF_SIGNAL_TO_THE_NUCLEUS
## 1085 REACTOME_SIGNALING_BY_FGFR3_FUSIONS_IN_CANCER
## 132 REACTOME_BRANCHED_CHAIN_AMINO_ACID_CATABOLISM
## 173 REACTOME_CHK1_CHK2_CDS1_MEDIATED_INACTIVATION_OF_CYCLIN_B_CDK1_COMPLEX
## 278 REACTOME_DEUBIQUITINATION
## 992 REACTOME_RNA_POLYMERASE_III_CHAIN_ELONGATION
## 993 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION
## 995 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_INITIATION_FROM_TYPE_3_PROMOTER
## 1005 REACTOME_RRNA_MODIFICATION_IN_THE_NUCLEUS_AND_CYTOSOL
## 39 REACTOME_ACTIVATION_OF_RAS_IN_B_CELLS
## 758 REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_TO_CONTROL_BILE_ACID_HOMEOSTASIS
## 684 REACTOME_MRNA_DECAY_BY_3_TO_5_EXORIBONUCLEASE
## 1238 REACTOME_TNFR1_MEDIATED_CERAMIDE_PRODUCTION
## 1344 REACTOME_YAP1_AND_WWTR1_TAZ_STIMULATED_GENE_EXPRESSION
## 514 REACTOME_IKBA_VARIANT_LEADS_TO_EDA_ID
## 528 REACTOME_INOSITOL_PHOSPHATE_METABOLISM
## 99 REACTOME_ASYMMETRIC_LOCALIZATION_OF_PCP_PROTEINS
## 1026 REACTOME_RUNX3_REGULATES_IMMUNE_RESPONSE_AND_CELL_MIGRATION
## 813 REACTOME_PHOSPHOLIPASE_C_MEDIATED_CASCADE_FGFR2
## 814 REACTOME_PHOSPHOLIPASE_C_MEDIATED_CASCADE_FGFR4
## 1278 REACTOME_TRANSFERRIN_ENDOCYTOSIS_AND_RECYCLING
## 1303 REACTOME_TRISTETRAPROLIN_TTP_ZFP36_BINDS_AND_DESTABILIZES_MRNA
## 179 REACTOME_CHREBP_ACTIVATES_METABOLIC_GENE_EXPRESSION
## 530 REACTOME_INSULIN_PROCESSING
## 668 REACTOME_MITOCHONDRIAL_IRON_SULFUR_CLUSTER_BIOGENESIS
## 1016 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_DIFFERENTIATION_OF_KERATINOCYTES
## 1315 REACTOME_UB_SPECIFIC_PROCESSING_PROTEASES
## 560 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING
## 1268 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_MECP2
## 936 REACTOME_REGULATION_OF_MECP2_EXPRESSION_AND_ACTIVITY
## 125 REACTOME_BINDING_OF_TCF_LEF_CTNNB1_TO_TARGET_GENE_PROMOTERS
## 123 REACTOME_BILE_ACID_AND_BILE_SALT_METABOLISM
## 743 REACTOME_NONCANONICAL_ACTIVATION_OF_NOTCH3
## 641 REACTOME_METABOLISM_OF_COFACTORS
## 1176 REACTOME_SYNTHESIS_OF_ACTIVE_UBIQUITIN_ROLES_OF_E1_AND_E2_ENZYMES
## 247 REACTOME_DEATH_RECEPTOR_SIGNALLING
## 702 REACTOME_NECTIN_NECL_TRANS_HETERODIMERIZATION
## 1060 REACTOME_SIALIC_ACID_METABOLISM
## 580 REACTOME_IRS_ACTIVATION
## 174 REACTOME_CHL1_INTERACTIONS
## 1004 REACTOME_RRNA_MODIFICATION_IN_THE_MITOCHONDRION
## 311 REACTOME_DOWNSTREAM_SIGNAL_TRANSDUCTION
## 1260 REACTOME_TRANS_GOLGI_NETWORK_VESICLE_BUDDING
## 177 REACTOME_CHONDROITIN_SULFATE_BIOSYNTHESIS
## 255 REACTOME_DEFECTIVE_CHST3_CAUSES_SEDCJD
## 437 REACTOME_GABA_SYNTHESIS_RELEASE_REUPTAKE_AND_DEGRADATION
## 550 REACTOME_INTERLEUKIN_18_SIGNALING
## 904 REACTOME_RECEPTOR_MEDIATED_MITOPHAGY
## 917 REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT
## 1335 REACTOME_VLDL_CLEARANCE
## 238 REACTOME_CYTOSOLIC_TRNA_AMINOACYLATION
## 393 REACTOME_FGFR3B_LIGAND_BINDING_AND_ACTIVATION
## 604 REACTOME_LOSS_OF_MECP2_BINDING_ABILITY_TO_THE_NCOR_SMRT_COMPLEX
## 1305 REACTOME_TRNA_MODIFICATION_IN_THE_MITOCHONDRION
## 60 REACTOME_AKT_PHOSPHORYLATES_TARGETS_IN_THE_NUCLEUS
## 415 REACTOME_FRS_MEDIATED_FGFR1_SIGNALING
## 416 REACTOME_FRS_MEDIATED_FGFR2_SIGNALING
## 417 REACTOME_FRS_MEDIATED_FGFR3_SIGNALING
## 418 REACTOME_FRS_MEDIATED_FGFR4_SIGNALING
## 651 REACTOME_METABOLISM_OF_WATER_SOLUBLE_VITAMINS_AND_COFACTORS
## 800 REACTOME_PECAM1_INTERACTIONS
## 809 REACTOME_PHASE_II_CONJUGATION_OF_COMPOUNDS
## 1012 REACTOME_RUNX1_REGULATES_EXPRESSION_OF_COMPONENTS_OF_TIGHT_JUNCTIONS
## 1191 REACTOME_SYNTHESIS_OF_PG
## 524 REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT
## 1197 REACTOME_SYNTHESIS_OF_PIPS_AT_THE_PLASMA_MEMBRANE
## 707 REACTOME_NEF_MEDIATED_DOWNREGULATION_OF_MHC_CLASS_I_COMPLEX_CELL_SURFACE_EXPRESSION
## 920 REACTOME_REGULATION_OF_CYTOSKELETAL_REMODELING_AND_CELL_SPREADING_BY_IPP_COMPLEX_COMPONENTS
## 298 REACTOME_DNA_DAMAGE_REVERSAL
## 567 REACTOME_INTRA_GOLGI_TRAFFIC
## 994 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_INITIATION_FROM_TYPE_1_PROMOTER
## 582 REACTOME_JNK_C_JUN_KINASES_PHOSPHORYLATION_AND_ACTIVATION_MEDIATED_BY_ACTIVATED_HUMAN_TAK1
## 330 REACTOME_EICOSANOID_LIGAND_BINDING_RECEPTORS
## 554 REACTOME_INTERLEUKIN_21_SIGNALING
## 555 REACTOME_INTERLEUKIN_23_SIGNALING
## 564 REACTOME_INTERLEUKIN_9_SIGNALING
## 336 REACTOME_EPH_EPHRIN_MEDIATED_REPULSION_OF_CELLS
## 914 REACTOME_REELIN_SIGNALLING_PATHWAY
## 460 REACTOME_GLYCEROPHOSPHOLIPID_BIOSYNTHESIS
## 274 REACTOME_DEPOLYMERISATION_OF_THE_NUCLEAR_LAMINA
## 383 REACTOME_FGFR1_LIGAND_BINDING_AND_ACTIVATION
## 385 REACTOME_FGFR1B_LIGAND_BINDING_AND_ACTIVATION
## 386 REACTOME_FGFR1C_LIGAND_BINDING_AND_ACTIVATION
## 388 REACTOME_FGFR2_LIGAND_BINDING_AND_ACTIVATION
## 390 REACTOME_FGFR2B_LIGAND_BINDING_AND_ACTIVATION
## 391 REACTOME_FGFR2C_LIGAND_BINDING_AND_ACTIVATION
## 392 REACTOME_FGFR3_LIGAND_BINDING_AND_ACTIVATION
## 867 REACTOME_PROCESSING_OF_SMDT1
## 171 REACTOME_CGMP_EFFECTS
## 740 REACTOME_NITRIC_OXIDE_STIMULATES_GUANYLATE_CYCLASE
## 1032 REACTOME_SARS_COV_1_GENOME_REPLICATION_AND_TRANSCRIPTION
## 924 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_BETA_CELLS
## 64 REACTOME_AMINO_ACID_CONJUGATION
## 134 REACTOME_BUTYRATE_RESPONSE_FACTOR_1_BRF1_BINDS_AND_DESTABILIZES_MRNA
## 206 REACTOME_CONJUGATION_OF_BENZOATE_WITH_GLYCINE
## 738 REACTOME_NICOTINAMIDE_SALVAGING
## 934 REACTOME_REGULATION_OF_KIT_SIGNALING
## 1216 REACTOME_TETRAHYDROBIOPTERIN_BH4_SYNTHESIS_RECYCLING_SALVAGE_AND_REGULATION
## 1186 REACTOME_SYNTHESIS_OF_LEUKOTRIENES_LT_AND_EOXINS_EX
## 602 REACTOME_LOSS_OF_FUNCTION_OF_MECP2_IN_RETT_SYNDROME
## 878 REACTOME_PROTEIN_UBIQUITINATION
## 1333 REACTOME_VITAMIN_D_CALCIFEROL_METABOLISM
## 649 REACTOME_METABOLISM_OF_STEROIDS
## 512 REACTOME_HYALURONAN_METABOLISM
## 513 REACTOME_HYALURONAN_UPTAKE_AND_DEGRADATION
## 911 REACTOME_RECYCLING_OF_EIF2_GDP
## 48 REACTOME_ACYL_CHAIN_REMODELLING_OF_PS
## 1090 REACTOME_SIGNALING_BY_INSULIN_RECEPTOR
## 943 REACTOME_REGULATION_OF_PYRUVATE_DEHYDROGENASE_PDH_COMPLEX
## 1101 REACTOME_SIGNALING_BY_NOTCH1
## 144 REACTOME_CARGO_TRAFFICKING_TO_THE_PERICILIARY_MEMBRANE
## 747 REACTOME_NOSTRIN_MEDIATED_ENOS_TRAFFICKING
## 766 REACTOME_NUCLEAR_ENVELOPE_NE_REASSEMBLY
## 901 REACTOME_RAS_PROCESSING
## 598 REACTOME_LINOLEIC_ACID_LA_METABOLISM
## 1070 REACTOME_SIGNALING_BY_EGFR
## 1330 REACTOME_VITAMIN_B2_RIBOFLAVIN_METABOLISM
## 178 REACTOME_CHONDROITIN_SULFATE_DERMATAN_SULFATE_METABOLISM
## 1307 REACTOME_TRNA_PROCESSING
## 697 REACTOME_N_GLYCAN_TRIMMING_AND_ELONGATION_IN_THE_CIS_GOLGI
## 128 REACTOME_BIOSYNTHESIS_OF_THE_N_GLYCAN_PRECURSOR_DOLICHOL_LIPID_LINKED_OLIGOSACCHARIDE_LLO_AND_TRANSFER_TO_A_NASCENT_PROTEIN
## 233 REACTOME_CYCLIN_D_ASSOCIATED_EVENTS_IN_G1
## 741 REACTOME_NOD1_2_SIGNALING_PATHWAY
## 751 REACTOME_NOTCH2_INTRACELLULAR_DOMAIN_REGULATES_TRANSCRIPTION
## 611 REACTOME_MAP3K8_TPL2_DEPENDENT_MAPK1_3_ACTIVATION
## 1192 REACTOME_SYNTHESIS_OF_PI
## 208 REACTOME_CONSTITUTIVE_SIGNALING_BY_EGFRVIII
## 221 REACTOME_CREB1_PHOSPHORYLATION_THROUGH_NMDA_RECEPTOR_MEDIATED_ACTIVATION_OF_RAS_SIGNALING
## 1313 REACTOME_TYROSINE_CATABOLISM
## 637 REACTOME_METABOLISM_OF_AMINE_DERIVED_HORMONES
## 1228 REACTOME_THYROXINE_BIOSYNTHESIS
## 1056 REACTOME_SHC_RELATED_EVENTS_TRIGGERED_BY_IGF1R
## 439 REACTOME_GAMMA_CARBOXYLATION_HYPUSINE_FORMATION_AND_ARYLSULFATASE_ACTIVATION
## 733 REACTOME_NEUROTRANSMITTER_RELEASE_CYCLE
## 1271 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_RUNX3
## 1323 REACTOME_UREA_CYCLE
## 752 REACTOME_NOTCH3_ACTIVATION_AND_TRANSMISSION_OF_SIGNAL_TO_THE_NUCLEUS
## 19 REACTOME_ACTIVATED_NTRK3_SIGNALS_THROUGH_PI3K
## 242 REACTOME_DARPP_32_EVENTS
## 294 REACTOME_DISPLACEMENT_OF_DNA_GLYCOSYLASE_BY_APEX1
## 397 REACTOME_FOLDING_OF_ACTIN_BY_CCT_TRIC
## 875 REACTOME_PROTEIN_METHYLATION
## 1089 REACTOME_SIGNALING_BY_HIPPO
## 1118 REACTOME_SIGNALING_BY_RNF43_MUTANTS
## 1301 REACTOME_TRIGLYCERIDE_CATABOLISM
## 1302 REACTOME_TRIGLYCERIDE_METABOLISM
## 508 REACTOME_HSF1_ACTIVATION
## 372 REACTOME_FATTY_ACIDS_BOUND_TO_GPR40_FFAR1_REGULATE_INSULIN_SECRETION
## 819 REACTOME_PI_3K_CASCADE_FGFR1
## 820 REACTOME_PI_3K_CASCADE_FGFR2
## 821 REACTOME_PI_3K_CASCADE_FGFR3
## 822 REACTOME_PI_3K_CASCADE_FGFR4
## 96 REACTOME_ASSEMBLY_OF_THE_HIV_VIRION
## 633 REACTOME_MET_INTERACTS_WITH_TNS_PROTEINS
## 16 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FYN
## 764 REACTOME_NTRK2_ACTIVATES_RAC1
## 1058 REACTOME_SHC1_EVENTS_IN_ERBB2_SIGNALING
## 595 REACTOME_LATE_ENDOSOMAL_MICROAUTOPHAGY
## 285 REACTOME_DISEASES_ASSOCIATED_WITH_GLYCOSYLATION_PRECURSOR_BIOSYNTHESIS
## 340 REACTOME_EPHRIN_SIGNALING
## 1017 REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_DIFFERENTIATION_OF_MYELOID_CELLS
## 1107 REACTOME_SIGNALING_BY_NOTCH4
## 254 REACTOME_DEFECTIVE_CHST14_CAUSES_EDS_MUSCULOCONTRACTURAL_TYPE
## 1244 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_CELL_DEATH_GENES
## 455 REACTOME_GLUCURONIDATION
## 927 REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM
## 40 REACTOME_ACTIVATION_OF_SMO
## 347 REACTOME_ERK_MAPK_TARGETS
## 946 REACTOME_REGULATION_OF_RUNX2_EXPRESSION_AND_ACTIVITY
## 1038 REACTOME_SCF_SKP2_MEDIATED_DEGRADATION_OF_P27_P21
## 1073 REACTOME_SIGNALING_BY_ERBB2_ECD_MUTANTS
## 518 REACTOME_INCRETIN_SYNTHESIS_SECRETION_AND_INACTIVATION
## 671 REACTOME_MITOCHONDRIAL_TRNA_AMINOACYLATION
## 1204 REACTOME_SYNTHESIS_SECRETION_AND_INACTIVATION_OF_GLUCAGON_LIKE_PEPTIDE_1_GLP_1
## 1308 REACTOME_TRNA_PROCESSING_IN_THE_MITOCHONDRION
## 1255 REACTOME_TRAFFICKING_AND_PROCESSING_OF_ENDOSOMAL_TLR
## 779 REACTOME_ONCOGENE_INDUCED_SENESCENCE
## 1235 REACTOME_TNF_RECEPTOR_SUPERFAMILY_TNFSF_MEMBERS_MEDIATING_NON_CANONICAL_NF_KB_PATHWAY
## 1164 REACTOME_SUMOYLATION_OF_TRANSCRIPTION_FACTORS
## 1110 REACTOME_SIGNALING_BY_NTRKS
## 824 REACTOME_PI3K_AKT_ACTIVATION
## 620 REACTOME_MATURATION_OF_SARS_COV_1_SPIKE_PROTEIN
## 1281 REACTOME_TRANSLATION_OF_SARS_COV_1_STRUCTURAL_PROTEINS
## 862 REACTOME_PROCESSING_AND_ACTIVATION_OF_SUMO
## 1153 REACTOME_SUMO_IS_TRANSFERRED_FROM_E1_TO_E2_UBE2I_UBC9
## 1069 REACTOME_SIGNALING_BY_CYTOSOLIC_FGFR1_FUSION_MUTANTS
## 1203 REACTOME_SYNTHESIS_SECRETION_AND_DEACYLATION_OF_GHRELIN
## 1205 REACTOME_SYNTHESIS_SECRETION_AND_INACTIVATION_OF_GLUCOSE_DEPENDENT_INSULINOTROPIC_POLYPEPTIDE_GIP
## 1053 REACTOME_SHC_MEDIATED_CASCADE_FGFR1
## 1054 REACTOME_SHC_MEDIATED_CASCADE_FGFR3
## 1055 REACTOME_SHC_MEDIATED_CASCADE_FGFR4
## 227 REACTOME_CROSS_PRESENTATION_OF_SOLUBLE_EXOGENOUS_ANTIGENS_ENDOSOMES
## 286 REACTOME_DISEASES_ASSOCIATED_WITH_N_GLYCOSYLATION_OF_PROTEINS
## 799 REACTOME_PD_1_SIGNALING
## 618 REACTOME_MATURATION_OF_NUCLEOPROTEIN
## 469 REACTOME_GLYOXYLATE_METABOLISM_AND_GLYCINE_DEGRADATION
## 682 REACTOME_MOLYBDENUM_COFACTOR_BIOSYNTHESIS
## 703 REACTOME_NEDDYLATION
## 1106 REACTOME_SIGNALING_BY_NOTCH3
## 136 REACTOME_CA_DEPENDENT_EVENTS
## 553 REACTOME_INTERLEUKIN_20_FAMILY_SIGNALING
## 1316 REACTOME_UCH_PROTEINASES
## 257 REACTOME_DEFECTIVE_CHSY1_CAUSES_TPBS
## 313 REACTOME_DOWNSTREAM_SIGNALING_OF_ACTIVATED_FGFR1
## 314 REACTOME_DOWNSTREAM_SIGNALING_OF_ACTIVATED_FGFR2
## 315 REACTOME_DOWNSTREAM_SIGNALING_OF_ACTIVATED_FGFR3
## 316 REACTOME_DOWNSTREAM_SIGNALING_OF_ACTIVATED_FGFR4
## 419 REACTOME_FRUCTOSE_CATABOLISM
## 656 REACTOME_METHIONINE_SALVAGE_PATHWAY
## 1075 REACTOME_SIGNALING_BY_ERBB4
## 448 REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES
## 612 REACTOME_MAPK_FAMILY_SIGNALING_CASCADES
## 889 REACTOME_PYRIMIDINE_CATABOLISM
## 494 REACTOME_HEME_BIOSYNTHESIS
## 1074 REACTOME_SIGNALING_BY_ERBB2_IN_CANCER
## 561 REACTOME_INTERLEUKIN_6_FAMILY_SIGNALING
## 562 REACTOME_INTERLEUKIN_6_SIGNALING
## 648 REACTOME_METABOLISM_OF_STEROID_HORMONES
## 127 REACTOME_BIOSYNTHESIS_OF_SPECIALIZED_PRORESOLVING_MEDIATORS_SPMS
## 692 REACTOME_MYD88_INDEPENDENT_TLR4_CASCADE
## 1325 REACTOME_VEGFR2_MEDIATED_CELL_PROLIFERATION
## 116 REACTOME_BETA_OXIDATION_OF_DECANOYL_COA_TO_OCTANOYL_COA_COA
## 119 REACTOME_BETA_OXIDATION_OF_OCTANOYL_COA_TO_HEXANOYL_COA
## 14 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_CDK5
## 74 REACTOME_ANTIGEN_PROCESSING_UBIQUITINATION_PROTEASOME_DEGRADATION
## 112 REACTOME_BBSOME_MEDIATED_CARGO_TARGETING_TO_CILIUM
## 210 REACTOME_CONSTITUTIVE_SIGNALING_BY_OVEREXPRESSED_ERBB2
## 236 REACTOME_CYTOSOLIC_SENSORS_OF_PATHOGEN_ASSOCIATED_DNA
## 240 REACTOME_DAP12_INTERACTIONS
## 269 REACTOME_DEGRADATION_OF_BETA_CATENIN_BY_THE_DESTRUCTION_COMPLEX
## 529 REACTOME_INSERTION_OF_TAIL_ANCHORED_PROTEINS_INTO_THE_ENDOPLASMIC_RETICULUM_MEMBRANE
## 548 REACTOME_INTERLEUKIN_15_SIGNALING
## 667 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION_OF_UNSATURATED_FATTY_ACIDS
## 978 REACTOME_RHO_GTPASES_ACTIVATE_NADPH_OXIDASES
## 1091 REACTOME_SIGNALING_BY_KIT_IN_DISEASE
## 1206 REACTOME_TAK1_ACTIVATES_NFKB_BY_PHOSPHORYLATION_AND_ACTIVATION_OF_IKKS_COMPLEX
## 414 REACTOME_FREE_FATTY_ACIDS_REGULATE_INSULIN_SECRETION
## 187 REACTOME_CITRIC_ACID_CYCLE_TCA_CYCLE
## 18 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_RAS
## 20 REACTOME_ACTIVATED_NTRK3_SIGNALS_THROUGH_RAS
## 1059 REACTOME_SHC1_EVENTS_IN_ERBB4_SIGNALING
## 260 REACTOME_DEFECTIVE_FACTOR_IX_CAUSES_HEMOPHILIA_B
## 446 REACTOME_GDP_FUCOSE_BIOSYNTHESIS
## 608 REACTOME_LYSOSOME_VESICLE_BIOGENESIS
## 1134 REACTOME_SLC_TRANSPORTER_DISORDERS
## 420 REACTOME_FRUCTOSE_METABOLISM
## 194 REACTOME_COBALAMIN_CBL_VITAMIN_B12_TRANSPORT_AND_METABOLISM
## 168 REACTOME_CELLULAR_RESPONSE_TO_HEAT_STRESS
## 663 REACTOME_MITOCHONDRIAL_BIOGENESIS
## 854 REACTOME_PP2A_MEDIATED_DEPHOSPHORYLATION_OF_KEY_METABOLIC_FACTORS
## 719 REACTOME_NEGATIVE_REGULATION_OF_NOTCH4_SIGNALING
## 1152 REACTOME_SUMO_IS_PROTEOLYTICALLY_PROCESSED
## 5 REACTOME_ABC_FAMILY_PROTEINS_MEDIATED_TRANSPORT
## 137 REACTOME_CA2_ACTIVATED_K_CHANNELS
## 226 REACTOME_CROSS_PRESENTATION_OF_PARTICULATE_EXOGENOUS_ANTIGENS_PHAGOSOMES
## 271 REACTOME_DEGRADATION_OF_DVL
## 348 REACTOME_ERKS_ARE_INACTIVATED
## 565 REACTOME_INTERLEUKIN_RECEPTOR_SHC_SIGNALING
## 657 REACTOME_METHYLATION
## 726 REACTOME_NETRIN_MEDIATED_REPULSION_SIGNALS
## 903 REACTOME_REACTIONS_SPECIFIC_TO_THE_COMPLEX_N_GLYCAN_SYNTHESIS_PATHWAY
## 928 REACTOME_REGULATION_OF_HSF1_MEDIATED_HEAT_SHOCK_RESPONSE
## 1150 REACTOME_SULFUR_AMINO_ACID_METABOLISM
## 760 REACTOME_NRAGE_SIGNALS_DEATH_THROUGH_JNK
## 475 REACTOME_GRB2_EVENTS_IN_ERBB2_SIGNALING
## 98 REACTOME_ASSOCIATION_OF_TRIC_CCT_WITH_TARGET_PROTEINS_DURING_BIOSYNTHESIS
## 6 REACTOME_ABC_TRANSPORTER_DISORDERS
## 603 REACTOME_LOSS_OF_FUNCTION_OF_SMAD2_3_IN_CANCER
## 947 REACTOME_REGULATION_OF_RUNX3_EXPRESSION_AND_ACTIVITY
## 1024 REACTOME_RUNX3_REGULATES_BCL2L11_BIM_TRANSCRIPTION
## 1025 REACTOME_RUNX3_REGULATES_CDKN1A_TRANSCRIPTION
## 17 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_PI3K
## 1132 REACTOME_SLBP_DEPENDENT_PROCESSING_OF_REPLICATION_DEPENDENT_HISTONE_PRE_MRNAS
## 1145 REACTOME_STABILIZATION_OF_P53
## 1076 REACTOME_SIGNALING_BY_ERYTHROPOIETIN
## 989 REACTOME_RNA_POLYMERASE_I_TRANSCRIPTION_TERMINATION
## 169 REACTOME_CELLULAR_RESPONSE_TO_HYPOXIA
## 408 REACTOME_FORMATION_OF_XYLULOSE_5_PHOSPHATE
## 1062 REACTOME_SIGNAL_ATTENUATION
## 646 REACTOME_METABOLISM_OF_POLYAMINES
## 735 REACTOME_NF_KB_ACTIVATION_THROUGH_FADD_RIP_1_PATHWAY_MEDIATED_BY_CASPASE_8_AND_10
## 804 REACTOME_PERK_REGULATES_GENE_EXPRESSION
## 38 REACTOME_ACTIVATION_OF_RAC1_DOWNSTREAM_OF_NMDARS
## 229 REACTOME_CS_DS_DEGRADATION
## 428 REACTOME_G1_S_DNA_DAMAGE_CHECKPOINTS
## 474 REACTOME_GPVI_MEDIATED_ACTIVATION_CASCADE
## 100 REACTOME_ATF4_ACTIVATES_GENES_IN_RESPONSE_TO_ENDOPLASMIC_RETICULUM_STRESS
## 252 REACTOME_DEFECTIVE_C1GALT1C1_CAUSES_TN_POLYAGGLUTINATION_SYNDROME_TNPS
## 456 REACTOME_GLUTAMATE_AND_GLUTAMINE_METABOLISM
## 186 REACTOME_CIRCADIAN_CLOCK
## 384 REACTOME_FGFR1_MUTANT_RECEPTOR_ACTIVATION
## 493 REACTOME_HEDGEHOG_ON_STATE
## 1080 REACTOME_SIGNALING_BY_FGFR1_IN_DISEASE
## 1251 REACTOME_TRAF3_DEPENDENT_IRF_ACTIVATION_PATHWAY
## 1254 REACTOME_TRAF6_MEDIATED_NF_KB_ACTIVATION
## 45 REACTOME_ACYL_CHAIN_REMODELING_OF_CL
## 826 REACTOME_PI5P_REGULATES_TP53_ACETYLATION
## 43 REACTOME_ACTIVATION_OF_THE_PHOTOTRANSDUCTION_CASCADE
## 859 REACTOME_PREGNENOLONE_BIOSYNTHESIS
## 1146 REACTOME_STAT5_ACTIVATION
## 1253 REACTOME_TRAF6_MEDIATED_IRF7_ACTIVATION
## 547 REACTOME_INTERLEUKIN_12_SIGNALING
## 117 REACTOME_BETA_OXIDATION_OF_HEXANOYL_COA_TO_BUTANOYL_COA
## 118 REACTOME_BETA_OXIDATION_OF_LAUROYL_COA_TO_DECANOYL_COA_COA
## 248 REACTOME_DECTIN_1_MEDIATED_NONCANONICAL_NF_KB_SIGNALING
## 1066 REACTOME_SIGNALING_BY_BMP
## 1223 REACTOME_THE_PHOTOTRANSDUCTION_CASCADE
## 1108 REACTOME_SIGNALING_BY_NTRK2_TRKB
## 1339 REACTOME_WAX_AND_PLASMALOGEN_BIOSYNTHESIS
## 1008 REACTOME_RSK_ACTIVATION
## 1142 REACTOME_SOS_MEDIATED_SIGNALLING
## 666 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION_OF_SATURATED_FATTY_ACIDS
## 272 REACTOME_DEGRADATION_OF_GLI1_BY_THE_PROTEASOME
## 546 REACTOME_INTERLEUKIN_12_FAMILY_SIGNALING
## 422 REACTOME_G_ALPHA_Q_SIGNALLING_EVENTS
## 410 REACTOME_FOXO_MEDIATED_TRANSCRIPTION
## 1160 REACTOME_SUMOYLATION_OF_INTRACELLULAR_RECEPTORS
## 929 REACTOME_REGULATION_OF_IFNA_SIGNALING
## 192 REACTOME_CLEC7A_DECTIN_1_SIGNALING
## 1232 REACTOME_TIE2_SIGNALING
## 1239 REACTOME_TNFR2_NON_CANONICAL_NF_KB_PATHWAY
## 1128 REACTOME_SIGNALING_BY_WNT_IN_CANCER
## 937 REACTOME_REGULATION_OF_MRNA_STABILITY_BY_PROTEINS_THAT_BIND_AU_RICH_ELEMENTS
## 1258 REACTOME_TRAFFICKING_OF_MYRISTOYLATED_PROTEINS_TO_THE_CILIUM
## 253 REACTOME_DEFECTIVE_CFTR_CAUSES_CYSTIC_FIBROSIS
## 1057 REACTOME_SHC1_EVENTS_IN_EGFR_SIGNALING
## 447 REACTOME_GENE_AND_PROTEIN_EXPRESSION_BY_JAK_STAT_SIGNALING_AFTER_INTERLEUKIN_12_STIMULATION
## 798 REACTOME_PCP_CE_PATHWAY
## 559 REACTOME_INTERLEUKIN_37_SIGNALING
## 491 REACTOME_HEDGEHOG_LIGAND_BIOGENESIS
## 1304 REACTOME_TRNA_AMINOACYLATION
## 378 REACTOME_FCERI_MEDIATED_NF_KB_ACTIVATION
## 1114 REACTOME_SIGNALING_BY_PDGFRA_TRANSMEMBRANE_JUXTAMEMBRANE_AND_KINASE_DOMAIN_MUTANTS
## 1151 REACTOME_SUMO_IS_CONJUGATED_TO_E1_UBA2_SAE1
## 1137 REACTOME_SMALL_INTERFERING_RNA_SIRNA_BIOGENESIS
## 268 REACTOME_DEGRADATION_OF_AXIN
## 47 REACTOME_ACYL_CHAIN_REMODELLING_OF_PI
## 151 REACTOME_CD163_MEDIATING_AN_ANTI_INFLAMMATORY_RESPONSE
## 293 REACTOME_DISORDERS_OF_TRANSMEMBRANE_TRANSPORTERS
## 360 REACTOME_ETHANOL_OXIDATION
## 81 REACTOME_APEX1_INDEPENDENT_RESOLUTION_OF_AP_SITES_VIA_THE_SINGLE_NUCLEOTIDE_REPLACEMENT_PATHWAY
## 544 REACTOME_INTERLEUKIN_1_SIGNALING
## 1124 REACTOME_SIGNALING_BY_THE_B_CELL_RECEPTOR_BCR
## 1209 REACTOME_TCR_SIGNALING
## 312 REACTOME_DOWNSTREAM_SIGNALING_EVENTS_OF_B_CELL_RECEPTOR_BCR
## 942 REACTOME_REGULATION_OF_PTEN_STABILITY_AND_ACTIVITY
## 105 REACTOME_AUF1_HNRNP_D0_BINDS_AND_DESTABILIZES_MRNA
## 239 REACTOME_DAG_AND_IP3_SIGNALING
## 556 REACTOME_INTERLEUKIN_27_SIGNALING
## 558 REACTOME_INTERLEUKIN_35_SIGNALLING
## 944 REACTOME_REGULATION_OF_RAS_BY_GAPS
## 89 REACTOME_ARACHIDONIC_ACID_METABOLISM
## 113 REACTOME_BETA_CATENIN_INDEPENDENT_WNT_SIGNALING
## 445 REACTOME_GASTRIN_CREB_SIGNALLING_PATHWAY_VIA_PKC_AND_MAPK
## 1182 REACTOME_SYNTHESIS_OF_GDP_MANNOSE
## 918 REACTOME_REGULATION_OF_CHOLESTEROL_BIOSYNTHESIS_BY_SREBP_SREBF
## 328 REACTOME_EGFR_TRANSACTIVATION_BY_GASTRIN
## 353 REACTOME_ERYTHROPOIETIN_ACTIVATES_RAS
## 1120 REACTOME_SIGNALING_BY_SCF_KIT
## 135 REACTOME_C_TYPE_LECTIN_RECEPTORS_CLRS
## pval_iDUX4dmso_WTdmso padj_iDUX4dmso_WTdmso ES_iDUX4dmso_WTdmso
## 21 1.962806e-06 9.083686e-05 -0.6804077
## 24 1.607477e-07 3.143281e-05 -0.6877521
## 108 2.715046e-06 1.118206e-04 -0.6151938
## 109 1.626615e-05 4.263426e-04 -0.6349016
## 110 3.031167e-06 1.135004e-04 -0.7549700
## 170 1.183244e-03 1.715067e-02 -0.4482367
## 181 4.279577e-06 1.518124e-04 -0.5960109
## 205 1.621075e-07 3.143281e-05 -0.6929581
## 275 4.724679e-05 1.044077e-03 -0.6957068
## 299 2.502571e-02 1.747909e-01 -0.4687977
## 300 3.264535e-07 3.143281e-05 -0.6987460
## 341 1.240886e-06 6.690860e-05 -0.5832310
## 346 1.928972e-06 9.083686e-05 -0.6337363
## 404 7.715889e-06 2.363868e-04 -0.6173947
## 483 4.798831e-07 3.593791e-05 -0.6735817
## 487 1.648903e-07 3.143281e-05 -0.8323887
## 504 1.597086e-06 8.280276e-05 -0.6228373
## 521 1.676273e-05 4.263426e-04 -0.6879882
## 624 3.161178e-07 3.143281e-05 -0.6383128
## 625 3.220526e-07 3.143281e-05 -0.6713978
## 709 1.114541e-06 6.260007e-05 -0.6209321
## 788 7.291131e-06 2.340106e-04 -0.5893461
## 847 5.187120e-06 1.748059e-04 -0.5813238
## 855 3.258980e-07 3.143281e-05 -0.7007887
## 856 1.639968e-05 4.263426e-04 -0.5867670
## 865 8.091910e-07 4.742563e-05 -0.6744055
## 906 3.031167e-06 1.135004e-04 -0.7549700
## 958 3.156841e-07 3.143281e-05 -0.6311610
## 980 4.646931e-05 1.044011e-03 -0.5583156
## 986 2.737447e-06 1.118206e-04 -0.6366616
## 987 8.049945e-06 2.411406e-04 -0.5820680
## 1013 2.093342e-05 4.950570e-04 -0.6056671
## 1131 4.579882e-06 1.582995e-04 -0.6679842
## 1213 2.009109e-05 4.924143e-04 -0.5881665
## 1272 2.958676e-06 1.135004e-04 -0.5693014
## 1275 1.229970e-05 3.527659e-04 -0.6256356
## 355 6.632595e-06 2.180668e-04 -0.5092564
## 626 2.056567e-05 4.950451e-04 -0.6605813
## 744 1.978684e-06 9.083686e-05 -0.7002166
## 431 6.461656e-07 4.377679e-05 -0.6727323
## 985 1.594531e-07 3.143281e-05 -0.6580429
## 405 2.410050e-07 3.143281e-05 0.9131373
## 479 1.594531e-07 3.143281e-05 -0.6677623
## 520 2.904978e-07 3.143281e-05 -0.4871438
## 587 2.410050e-07 3.143281e-05 0.9131373
## 832 1.632267e-07 3.143281e-05 -0.7414497
## 162 3.961208e-07 3.559806e-05 -0.4077302
## 161 4.326238e-07 3.585858e-05 -0.4811119
## 430 4.522224e-07 3.585858e-05 -0.5359285
## 160 6.495073e-07 4.377679e-05 -0.3960829
## 482 7.755540e-07 4.742563e-05 -0.5879258
## 647 7.424927e-07 4.742563e-05 -0.3585089
## 1031 2.256469e-04 4.473117e-03 -0.4639407
## 357 2.021592e-06 9.083686e-05 -0.5784570
## 1048 5.753653e-05 1.250956e-03 -0.5592039
## 366 2.487404e-06 1.081619e-04 0.4694275
## 1144 1.619737e-03 2.314461e-02 -0.4191795
## 180 3.780946e-06 1.377491e-04 -0.5221289
## 677 1.812732e-05 4.525115e-04 -0.4979315
## 362 1.038553e-05 3.043411e-04 -0.4795498
## 932 4.628580e-03 5.378729e-02 0.4361836
## 1155 7.531185e-06 2.360939e-04 -0.6102822
## 1111 8.491651e-05 1.788554e-03 -0.4491912
## 1154 6.682675e-05 1.429880e-03 -0.4895905
## 973 5.041348e-02 2.724515e-01 -0.3069271
## 967 1.651372e-02 1.334495e-01 0.3737118
## 1037 2.677202e-01 6.633948e-01 -0.5352821
## 745 3.418376e-05 7.810120e-04 -0.4850463
## 863 1.286778e-05 3.613701e-04 -0.4190481
## 44 5.653121e-03 6.047943e-02 -0.6756948
## 673 8.290818e-04 1.241780e-02 -0.4578803
## 200 1.439417e-05 3.959866e-04 0.6272081
## 1006 1.660017e-05 4.263426e-04 -0.4355312
## 480 2.632257e-04 5.068975e-03 -0.4655378
## 688 2.532239e-05 5.885273e-04 -0.4362949
## 965 5.309793e-04 9.060254e-03 -0.4555847
## 25 2.081866e-03 2.834702e-02 -0.7074114
## 1015 2.013673e-04 4.051390e-03 -0.4801831
## 361 3.054346e-04 5.798955e-03 -0.4647156
## 836 7.352636e-01 9.835183e-01 0.3088468
## 1041 4.193688e-03 5.002736e-02 -0.4012295
## 304 6.203188e-04 9.723136e-03 -0.6945487
## 1319 5.216278e-04 9.014799e-03 -0.8652939
## 204 4.062303e-04 7.292536e-03 -0.8282680
## 734 4.111519e-04 7.292536e-03 0.3279382
## 301 2.198550e-03 2.934302e-02 -0.4479557
## 609 3.163161e-04 5.922141e-03 -0.3619221
## 895 7.236199e-01 9.835183e-01 -0.2629894
## 197 1.542424e-04 3.198749e-03 0.6364581
## 535 1.569989e-04 3.206583e-03 0.5719845
## 1208 5.879058e-04 9.548156e-03 -0.4248772
## 1127 7.500517e-04 1.148943e-02 -0.3905199
## 124 8.180077e-02 3.686281e-01 0.4579748
## 481 2.513975e-04 4.911360e-03 -0.4509158
## 184 8.396363e-01 9.992585e-01 -0.3389029
## 1100 4.082004e-04 7.292536e-03 -0.4220674
## 495 9.224189e-02 3.947367e-01 0.2422828
## 1291 2.553491e-02 1.767374e-01 0.6941777
## 324 3.957484e-04 7.292536e-03 0.5921496
## 837 5.398940e-02 2.799143e-01 0.2983940
## 182 8.547556e-01 9.992585e-01 -0.3073685
## 1269 6.052364e-04 9.712603e-03 -0.4225415
## 921 4.810494e-04 8.421488e-03 -0.4057566
## 1035 1.928652e-01 5.764574e-01 -0.8171700
## 689 5.428922e-04 9.147733e-03 -0.5811964
## 568 5.514194e-04 9.176708e-03 0.7217233
## 563 5.610298e-04 9.222783e-03 -0.6969512
## 669 8.471085e-01 9.992585e-01 -0.2420509
## 95 6.136264e-04 9.723136e-03 0.6231354
## 273 6.661040e-04 1.032078e-02 0.5110084
## 1279 7.934294e-04 1.201734e-02 -0.3596474
## 203 2.554972e-01 6.558699e-01 0.4007289
## 42 7.855906e-03 7.962226e-02 -0.4490114
## 780 9.767029e-04 1.446808e-02 0.5466017
## 642 6.909267e-01 9.691667e-01 0.2796205
## 198 1.174623e-03 1.715067e-02 0.6792577
## 435 8.822105e-01 9.992585e-01 -0.2943614
## 724 9.757472e-01 9.992585e-01 -0.1716840
## 894 3.337389e-01 7.133898e-01 -0.3332165
## 303 7.756757e-02 3.544902e-01 -0.3750907
## 886 8.563502e-01 9.992585e-01 0.3497930
## 1067 1.631111e-03 2.314461e-02 0.5562822
## 801 1.888535e-02 1.430194e-01 0.6736390
## 93 1.736711e-03 2.413491e-02 0.6394198
## 199 1.722129e-03 2.413491e-02 0.5947683
## 676 1.324222e-01 4.936480e-01 -0.3156481
## 537 5.558555e-01 8.941246e-01 -0.4587623
## 701 1.900061e-03 2.613553e-02 0.7520042
## 1119 8.079633e-03 8.067663e-02 -0.3455293
## 320 8.743642e-02 3.834164e-01 -0.8823655
## 510 2.137739e-03 2.881672e-02 0.5130398
## 211 1.140357e-01 4.534518e-01 -0.6439514
## 402 4.766401e-02 2.655004e-01 0.6170744
## 331 2.289065e-03 2.953030e-02 0.6799353
## 442 2.279301e-03 2.953030e-02 0.7037954
## 1289 2.279301e-03 2.953030e-02 0.7037954
## 1311 2.300209e-03 2.953030e-02 0.9571367
## 851 4.296791e-01 7.967091e-01 -0.5119951
## 787 2.364175e-03 3.006517e-02 -0.7642679
## 1169 4.883796e-03 5.532232e-02 -0.4529851
## 680 7.039339e-01 9.722366e-01 -0.5050990
## 500 2.542296e-03 3.202818e-02 -0.4280946
## 486 7.889759e-01 9.912193e-01 -0.3622018
## 55 6.665189e-01 9.578545e-01 -0.4378395
## 364 1.719131e-02 1.360232e-01 -0.5509476
## 91 5.497712e-02 2.839431e-01 0.2788082
## 485 1.001935e-01 4.117708e-01 -0.8731401
## 1096 1.934154e-01 5.768229e-01 0.3330613
## 52 7.261076e-01 9.835183e-01 0.2897549
## 640 3.148404e-03 3.929674e-02 0.3542267
## 861 9.490076e-01 9.992585e-01 -0.2737837
## 425 9.801154e-02 4.040353e-01 -0.6559396
## 54 8.268203e-01 9.952257e-01 -0.3308476
## 655 4.109882e-02 2.456782e-01 0.9796487
## 436 9.128498e-01 9.992585e-01 0.2564486
## 1329 9.298229e-01 9.992585e-01 -0.2159019
## 811 3.768102e-03 4.660001e-02 0.9807717
## 1285 3.851979e-03 4.720425e-02 0.4755933
## 94 6.093327e-01 9.312704e-01 0.4346145
## 212 3.960120e-03 4.809226e-02 0.5745303
## 627 1.772818e-01 5.596625e-01 0.2206295
## 444 4.145923e-03 4.989915e-02 0.5867441
## 896 6.421360e-01 9.470453e-01 -0.2550577
## 1061 9.691966e-01 9.992585e-01 -0.2131839
## 65 4.483035e-03 5.254897e-02 0.9080530
## 228 4.454855e-03 5.254897e-02 0.8201506
## 794 4.752167e-03 5.428746e-02 -0.8991741
## 976 4.722107e-03 5.428746e-02 0.5974246
## 781 1.741066e-02 1.360232e-01 -0.4305709
## 681 5.004666e-03 5.621908e-02 0.6891390
## 1299 5.083308e-03 5.663057e-02 0.3613232
## 1183 5.235923e-03 5.785266e-02 0.8699339
## 165 5.298153e-03 5.806432e-02 0.5293119
## 59 5.379435e-03 5.847967e-02 0.8133950
## 407 5.471657e-03 5.900635e-02 0.6088035
## 7 2.719605e-02 1.835400e-01 -0.8344514
## 797 2.139490e-01 6.110240e-01 -0.4855665
## 368 1.886425e-01 5.714385e-01 -0.3165168
## 922 3.296893e-01 7.110738e-01 0.6721981
## 426 6.559777e-02 3.169383e-01 -0.6212756
## 566 2.306362e-01 6.293473e-01 0.2549545
## 834 5.070192e-01 8.673374e-01 0.2944838
## 1274 7.196759e-01 9.829009e-01 0.3011952
## 429 6.422210e-03 6.763390e-02 -0.7717857
## 1095 6.391842e-03 6.763390e-02 -0.9724373
## 571 7.764416e-01 9.892193e-01 -0.2914782
## 583 8.415237e-01 9.992585e-01 -0.3560108
## 71 6.035641e-01 9.254452e-01 0.2860077
## 817 2.392372e-02 1.697325e-01 -0.7410790
## 1039 6.559854e-03 6.854793e-02 0.5850560
## 593 2.997091e-02 1.951729e-01 -0.6491407
## 523 6.620351e-02 3.187226e-01 -0.6390215
## 952 9.347289e-01 9.992585e-01 -0.3483157
## 1077 5.510613e-01 8.938996e-01 -0.2991744
## 1081 5.510613e-01 8.938996e-01 -0.2991744
## 158 7.045175e-03 7.305305e-02 0.4488276
## 977 2.634385e-01 6.564049e-01 -0.5020327
## 939 7.404300e-03 7.619081e-02 -0.5887742
## 675 2.690403e-01 6.642241e-01 -0.2654308
## 290 7.633599e-03 7.795523e-02 0.3994417
## 742 8.036637e-03 8.067663e-02 0.5197459
## 223 2.173255e-01 6.141610e-01 -0.7358201
## 23 8.284546e-03 8.172834e-02 0.6316031
## 912 8.306218e-03 8.172834e-02 0.5238291
## 323 8.684352e-03 8.421947e-02 -0.7202489
## 610 8.628864e-03 8.421947e-02 0.5741802
## 960 2.764604e-01 6.700072e-01 -0.4406940
## 1267 9.027646e-03 8.692334e-02 -0.7590271
## 176 9.007817e-01 9.992585e-01 -0.4381830
## 833 8.544934e-01 9.992585e-01 0.2647555
## 343 9.274733e-03 8.866908e-02 0.3550939
## 949 9.369304e-03 8.894241e-02 -0.4649505
## 287 9.526315e-03 8.980051e-02 0.7253962
## 83 1.252208e-01 4.741506e-01 0.5253060
## 959 2.593464e-01 6.558699e-01 -0.4315105
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## 953 1.033248e-02 9.539851e-02 -0.5397007
## 1097 1.026734e-02 9.539851e-02 0.5522552
## 349 3.647511e-02 2.245134e-01 0.8706520
## 945 9.331606e-01 9.992585e-01 0.2454936
## 687 5.053493e-01 8.655793e-01 0.7476435
## 899 1.504985e-02 1.243222e-01 0.7447784
## 1159 3.755094e-01 7.562630e-01 0.5456910
## 35 1.094795e-02 1.003934e-01 0.4693445
## 53 8.415535e-01 9.992585e-01 -0.3132278
## 762 1.917551e-01 5.756051e-01 -0.6565874
## 853 4.873292e-01 8.484152e-01 0.7566410
## 225 1.138750e-02 1.033333e-01 -0.7498611
## 1210 1.142186e-02 1.033333e-01 -0.6629294
## 763 1.167823e-02 1.049484e-01 -0.5069911
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## 670 1.775122e-02 1.367351e-01 -0.4588657
## 890 8.077396e-01 9.945825e-01 0.3742702
## 58 1.215687e-02 1.071076e-01 0.5320554
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## 569 7.512148e-01 9.892193e-01 0.2792023
## 905 7.457427e-01 9.877613e-01 0.4034298
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## 342 8.968044e-01 9.992585e-01 -0.3250471
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## 467 1.279806e-02 1.120246e-01 0.5123237
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## 1171 1.322964e-02 1.150552e-01 0.5901648
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## 729 1.362384e-02 1.155027e-01 0.3693334
## 1133 1.349930e-02 1.155027e-01 0.5276371
## 1287 1.338051e-02 1.155027e-01 0.9134162
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## 746 3.203019e-01 7.078148e-01 0.6768822
## 1052 3.203019e-01 7.078148e-01 0.6768822
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## 135 -0.7847130 5554518
## pval_iDUX4t02dox_iDUX4dmso padj_iDUX4t02dox_iDUX4dmso
## 21 2.852660e-03 0.1215904296
## 24 2.942654e-02 0.3885065526
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## 181 7.539372e-02 0.5632842719
## 205 3.933231e-01 0.8673814453
## 275 1.046785e-01 0.6161859524
## 299 6.183149e-01 0.9910730565
## 300 5.262645e-02 0.4667135248
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## 346 8.966647e-04 0.0863359974
## 404 2.017817e-02 0.3358046741
## 483 3.131380e-02 0.3951992313
## 487 3.176368e-03 0.1259336352
## 504 8.779115e-01 0.9999157747
## 521 1.618136e-02 0.3183391168
## 624 9.245820e-02 0.5812956713
## 625 1.513540e-01 0.6791653207
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## 847 1.181036e-02 0.2948214631
## 855 8.157735e-03 0.2431459232
## 856 2.668596e-03 0.1215904296
## 865 7.240248e-01 0.9999157747
## 906 1.137503e-02 0.2893121512
## 958 7.120001e-02 0.5484434760
## 980 4.754525e-03 0.1732188944
## 986 1.961174e-02 0.3304578560
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## 1013 1.284252e-01 0.6632840918
## 1131 3.298549e-03 0.1270412439
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## 1272 1.262743e-01 0.6572116169
## 1275 2.380051e-01 0.7486148757
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## 673 2.083142e-01 0.7389673344
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## 688 4.465161e-01 0.9010533651
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## 25 2.752579e-01 0.7928368867
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## 1329 5.738286e-04 0.0703200902
## 811 3.826623e-01 0.8610341080
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## 94 2.361351e-01 0.7486148757
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## 1061 1.695675e-02 0.3183391168
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## 681 4.227589e-03 0.1582997060
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## 1081 3.752208e-01 0.8500800785
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## 977 6.899926e-01 0.9999157747
## 939 2.365891e-02 0.3665771881
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## 742 1.516896e-03 0.1080193331
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## 833 1.050467e-03 0.0885018814
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## 287 4.803231e-01 0.9194675010
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## 959 4.535019e-01 0.9038739416
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## pval_iDUX4t14dox_iDUX4dmso padj_iDUX4t14dox_iDUX4dmso
## 21 1.651335e-07 1.326828e-05
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## 194 4228366 8.196258e-01
## 168 3837662 3.814006e-01
## 663 3850077 4.506876e-01
## 854 2785891 3.730738e-01
## 719 2948441 6.937587e-01
## 1152 2537576 2.371515e-01
## 5 2992555 7.719574e-01
## 137 2667089 7.985386e-01
## 226 2602382 6.774242e-01
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## 348 3032725 3.286169e-01
## 565 2966835 4.066370e-01
## 657 3098854 5.536417e-01
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## 1150 2323306 1.051493e-01
## 760 3143509 5.265727e-01
## 475 3069583 3.914706e-01
## 98 5482957 9.632864e-01
## 6 5495698 6.543298e-01
## 603 4956339 9.875428e-01
## 947 4429990 8.159191e-01
## 1024 4956339 9.875428e-01
## 1025 4956339 9.875428e-01
## 17 4078639 3.976732e-01
## 1132 3452203 2.780200e-01
## 1145 5538748 8.171197e-01
## 1076 3203340 6.835593e-01
## 989 3239971 7.082179e-01
## 169 5579866 9.649297e-01
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## 646 5291947 9.270070e-01
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## 38 3132155 8.964364e-01
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## 100 4230605 6.070394e-01
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## 456 2657943 7.458677e-01
## 186 3640110 1.746244e-01
## 384 3676126 2.647579e-01
## 493 4857245 9.775404e-01
## 1080 4098153 7.430799e-01
## 1251 3492994 2.726199e-01
## 1254 4582826 7.550820e-01
## 45 5162800 6.703220e-01
## 826 3142727 3.002604e-01
## 43 3573323 4.462086e-01
## 859 4313616 9.814583e-01
## 1146 4333718 9.893055e-01
## 1253 2872753 6.157406e-01
## 547 2747038 8.135943e-01
## 117 5299664 3.773133e-01
## 118 5299664 3.773133e-01
## 248 5380368 6.706661e-01
## 1066 5074417 4.267757e-01
## 1223 3914620 9.846002e-01
## 1108 3567190 3.560401e-01
## 1339 3475168 8.914235e-01
## 1008 3151536 3.554560e-01
## 1142 3282600 5.694417e-01
## 666 5234292 8.627863e-01
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## 1160 3687684 3.897377e-01
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## 1232 4171132 8.613316e-01
## 1239 5197561 6.846309e-01
## 1128 3388687 5.097419e-01
## 937 4683491 5.587847e-01
## 1258 3638684 7.384838e-01
## 253 5634289 7.492062e-01
## 1057 5496976 4.806974e-01
## 447 3164539 8.068383e-01
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## 559 3245942 8.684256e-01
## 491 5635017 8.078921e-01
## 1304 3775553 9.727044e-01
## 378 5274529 6.831379e-01
## 1114 4425240 8.937013e-01
## 1151 5322010 3.316650e-01
## 1137 4006960 8.771208e-01
## 268 5539011 8.148055e-01
## 47 4549127 9.185029e-01
## 151 4697011 5.266734e-01
## 293 4338345 6.416299e-01
## 360 3554006 6.613978e-01
## 81 4284450 3.042853e-01
## 544 5409556 7.735885e-01
## 1124 5611215 9.852925e-01
## 1209 4473781 5.220930e-01
## 312 5621386 9.955804e-01
## 942 4926034 7.354656e-01
## 105 5611767 7.055217e-01
## 239 3897034 4.874452e-01
## 556 4654614 7.824495e-01
## 558 4654614 7.824495e-01
## 944 5114933 9.621280e-01
## 89 3476669 5.413713e-01
## 113 5536188 9.540230e-01
## 445 5605156 9.071088e-01
## 1182 4895019 4.523014e-01
## 918 3524117 9.839573e-01
## 328 5506901 5.036388e-01
## 353 4133503 8.441962e-01
## 1120 5411372 5.009046e-01
## 135 5314444 6.614923e-01
## padj_WTt14dox_WTdmso ES_WTt14dox_WTdmso NES_WTt14dox_WTdmso
## 21 0.0002963833 0.7022825 2.1654044
## 24 0.0002721416 0.6576017 2.1352856
## 108 0.0004694851 0.6364857 2.1018309
## 109 0.0006739158 0.6710387 2.0935823
## 110 0.0002601888 0.8015492 2.2010813
## 170 0.0004694851 0.5474629 1.9835113
## 181 0.0025881624 0.5850964 1.9616896
## 205 0.0005365814 0.6632144 2.1028964
## 275 0.0002601888 0.7712793 2.1860065
## 299 0.0016135689 0.6610630 2.0506036
## 300 0.0002601888 0.7040835 2.1840522
## 341 0.0104833644 0.5156340 1.8186683
## 346 0.0002601888 0.6607237 2.1454228
## 404 0.0002721416 0.6587860 2.1391311
## 483 0.2636150455 0.4722286 1.5530760
## 487 0.0559698171 0.5953994 1.7882201
## 504 0.0103912975 0.5686639 1.8778668
## 521 0.0002417661 0.7715416 2.2468887
## 624 0.0002384373 0.6680097 2.2554690
## 625 0.0002963833 0.6588613 2.1297540
## 709 0.0259282588 0.5390737 1.7940849
## 788 0.0002417661 0.6504882 2.1886821
## 847 0.0004694851 0.6011401 2.0625787
## 855 0.0002601888 0.6985615 2.1794511
## 856 0.0140103752 0.5535859 1.8423828
## 865 0.0224301994 0.5773735 1.8398814
## 906 0.0002601888 0.8015492 2.2010813
## 958 0.0002601888 0.6259018 2.1204024
## 980 0.0045132833 0.5654985 1.9157708
## 986 0.0015219238 0.6274920 2.0283534
## 987 0.0023940100 0.5758707 1.9573700
## 1013 0.0043705245 0.6050269 1.9557356
## 1131 0.0002417661 0.7177916 2.2132248
## 1213 0.0023940100 0.5991922 1.9786787
## 1272 0.0005659517 0.5758660 2.0155808
## 1275 0.0002417661 0.6903909 2.2000270
## 355 0.3227700025 0.3887674 1.4425197
## 626 0.0002384373 0.7795509 2.3413001
## 744 0.0002963833 0.7194231 2.1607124
## 431 0.0527996333 0.5502112 1.7619053
## 985 0.1480635983 0.4918757 1.6306715
## 405 0.0078871532 0.7566902 1.9737885
## 479 0.1630098926 0.4886178 1.6198711
## 520 0.9999914219 0.2046226 0.7997340
## 587 0.0078871532 0.7566902 1.9737885
## 832 0.0532760299 0.5731138 1.7777870
## 162 0.0280838340 0.3454194 1.4780828
## 161 0.6356681642 0.3271652 1.2899832
## 430 0.1824400412 0.4071050 1.5105615
## 160 0.0133647660 0.3460643 1.4975759
## 482 0.2607762526 0.4274064 1.5074846
## 647 0.9999914219 -0.1502856 -0.6983159
## 1031 0.9999914219 0.2912633 1.0752315
## 357 0.0280838340 0.4965575 1.7425472
## 1048 0.0007731335 0.6069588 2.0422199
## 366 0.9999914219 -0.1602257 -0.6364005
## 1144 0.9999914219 0.2083037 0.7815076
## 180 0.1223376188 0.4242250 1.5660748
## 677 0.0015168021 0.5114967 1.8915324
## 362 0.9999914219 -0.2149873 -0.8459874
## 932 0.3484571171 0.4309095 1.4873477
## 1155 0.5549536177 0.4352535 1.4256315
## 1111 0.8257109269 0.3307949 1.2651746
## 1154 0.9586165079 0.3388987 1.2374951
## 973 0.0106125120 0.4053287 1.6356040
## 967 0.9999914219 0.3391063 1.2157506
## 1037 0.9999914219 -0.3258985 -0.7159652
## 745 0.9999914219 -0.2158455 -0.8301548
## 863 0.9999914219 0.2101510 0.8562689
## 44 0.9999914219 0.4825414 1.1982512
## 673 0.9999914219 0.2155348 0.7793164
## 200 0.9999914219 0.1698580 0.5412763
## 1006 0.9999914219 -0.2015662 -0.8315976
## 480 0.1403854628 0.4236299 1.5556312
## 688 0.9999914219 0.2305174 0.9114232
## 965 0.9999914219 0.2148965 0.7876408
## 25 0.5215526774 0.6154812 1.5283685
## 1015 0.0129509188 0.4881446 1.7685953
## 361 0.9999914219 0.2090962 0.7663814
## 836 0.9999914219 0.4812106 1.1217410
## 1041 0.9999914219 0.2100943 0.7882254
## 304 0.9999914219 0.4031985 1.0667317
## 1319 0.9999914219 0.3923456 0.8078293
## 204 0.9999914219 0.3529264 0.7791206
## 734 0.9999914219 0.2462845 1.0310714
## 301 0.9999914219 0.3030587 1.0817478
## 609 0.0245147715 0.3630241 1.5226664
## 895 0.9999914219 -0.2209887 -0.6876539
## 197 0.9999914219 0.2635335 0.7914954
## 535 0.9999914219 0.3450407 1.1153360
## 1208 0.0364115940 0.4282687 1.6317105
## 1127 0.1912452156 0.3615177 1.4370252
## 124 0.9999914219 0.3830246 1.0748174
## 481 0.1311258251 0.4118411 1.5427797
## 184 0.9999914219 0.6187117 1.2200520
## 1100 0.3039836957 0.3678032 1.4171004
## 495 0.1631454949 0.3263676 1.3808100
## 1291 0.9999914219 0.3545865 0.7827854
## 324 0.9999914219 0.2726200 0.8405911
## 837 0.9999914219 0.2805835 1.0810536
## 182 0.9999914219 0.5482646 1.1717471
## 1269 0.0106125120 0.4502384 1.7198343
## 921 0.9999914219 0.1919223 0.7538256
## 1035 0.9999914219 0.8143214 1.2058894
## 689 0.9999914219 -0.2076290 -0.6630674
## 568 0.4688303597 0.5922580 1.5448754
## 563 0.4534711528 0.5884174 1.5567602
## 669 0.0093715468 0.6707177 1.9532685
## 95 0.9999914219 -0.2188644 -0.6607331
## 273 0.9999914219 0.2519383 0.8416393
## 1279 0.9999914219 0.2098690 0.8699218
## 203 0.9999914219 0.2686803 0.7108403
## 42 0.9999914219 0.1850425 0.6309611
## 780 0.9999914219 0.2191702 0.6949369
## 642 0.9999914219 -0.3061214 -0.8533416
## 198 0.9999914219 0.3067402 0.8222909
## 435 0.9999914219 -0.4887214 -1.0736702
## 724 0.9999914219 0.2366130 1.0198945
## 894 0.2096804732 -0.5081907 -1.6324844
## 303 0.9999914219 0.2953289 1.0070175
## 886 0.9999914219 0.5594274 0.9843912
## 1067 0.9999914219 0.2273854 0.7011157
## 801 0.9999914219 0.4623337 1.0777374
## 93 0.9999914219 0.3925401 1.0900692
## 199 0.9999914219 0.3935624 1.1555628
## 676 0.9999914219 0.3235275 1.2004475
## 537 0.9999914219 0.5231604 1.0316322
## 701 0.9999914219 0.3769461 0.8992722
## 1119 0.9999914219 0.2145171 0.8624143
## 320 0.9999914219 0.5872292 0.8695995
## 510 0.9999914219 0.3437118 1.1059168
## 211 0.9999914219 -0.4150103 -0.8791959
## 402 0.9999914219 0.3495294 0.8147813
## 331 0.9999914219 -0.4843252 -1.2966179
## 442 0.9999914219 0.4848904 1.2255809
## 1289 0.9999914219 0.4848904 1.2255809
## 1311 0.9999914219 0.5439315 0.8858979
## 851 0.9999914219 -0.5972513 -1.2133072
## 787 0.9999914219 0.3743850 0.8505732
## 1169 0.9999914219 0.2453122 0.8467313
## 680 0.9999914219 0.7261520 1.1826794
## 500 0.9999914219 0.2571235 0.9406708
## 486 0.9999914219 0.4739832 0.9346584
## 55 0.9999914219 -0.4558908 -0.8810686
## 364 0.9999914219 0.4225596 1.1734323
## 91 0.9999914219 -0.2062541 -0.8560122
## 485 0.9999914219 0.7791356 1.1537844
## 1096 0.9999914219 0.2257225 0.7262778
## 52 0.9999914219 -0.3198144 -0.8295787
## 640 0.9999914219 0.2499714 0.9729146
## 861 0.9999914219 0.5372332 1.0593827
## 425 0.9999914219 -0.4452457 -0.9432493
## 54 0.9999914219 -0.4990712 -1.0572783
## 655 0.9999914219 -0.5228296 -0.6970675
## 436 0.9999914219 -0.4853902 -1.1007350
## 1329 0.9999914219 -0.2667461 -0.7916570
## 811 0.9999914219 0.6569468 0.9728410
## 1285 0.9999914219 0.3609820 1.1920506
## 94 0.9999914219 -0.3847059 -0.7434944
## 212 0.3553412026 0.5387256 1.5688803
## 627 0.9999914219 0.2569466 1.1148372
## 444 0.9999914219 0.4090835 1.1704807
## 896 0.9999914219 -0.3333633 -1.1530102
## 1061 0.9999914219 -0.3129996 -0.7824201
## 65 0.9999914219 0.5842224 1.0280214
## 228 0.9999914219 -0.6254474 -1.3250052
## 794 0.9999914219 0.7208064 1.2683601
## 976 0.9999914219 0.3727107 1.0458755
## 781 0.9999914219 0.2817779 0.9545947
## 681 0.9999914219 -0.3910174 -0.9964687
## 1299 0.9999914219 -0.2325784 -0.9164801
## 1183 0.9999914219 -0.3936634 -0.7608061
## 165 0.9631989249 0.4260286 1.2970456
## 59 0.9999914219 0.5251257 1.0812199
## 407 0.9999914219 0.3877668 1.0648207
## 7 0.9999914219 0.4851562 0.8537004
## 797 0.9999914219 0.5283568 1.2871472
## 368 0.3528084627 0.4086086 1.4585014
## 922 0.9999914219 -0.7344941 -1.2381787
## 426 0.9999914219 -0.3150149 -0.7348891
## 566 0.9999914219 0.2339889 0.9004705
## 834 0.9999914219 0.3153562 0.9259368
## 1274 0.9999914219 -0.5072766 -1.2680636
## 429 0.9999914219 -0.4255620 -0.9349155
## 1095 0.9999914219 0.5864065 0.8683812
## 571 0.9999914219 0.2816529 0.7118896
## 583 0.9999914219 0.6183098 1.1580376
## 71 0.9999914219 0.2924828 0.8289725
## 817 0.9999914219 -0.5327932 -1.1287180
## 1039 0.9999914219 0.4254393 1.1938388
## 593 0.9999914219 0.4200267 0.9791164
## 523 0.9999914219 -0.3925772 -0.8902601
## 952 0.9999914219 0.7081755 1.1534012
## 1077 0.9999914219 0.3586110 1.0770507
## 1081 0.9999914219 0.3586110 1.0770507
## 158 0.9999914219 0.3543830 1.1838721
## 977 0.9999914219 0.4536958 1.0307609
## 939 0.9999914219 -0.3577347 -1.0205277
## 675 0.9999914219 0.2739433 1.0831213
## 290 0.9999914219 0.2995788 1.0669260
## 742 0.9999914219 -0.2790776 -0.8684098
## 223 0.9999914219 -0.2424725 -0.4087497
## 23 0.9999914219 0.4198178 1.0950737
## 912 0.9999914219 0.4017946 1.2067482
## 323 0.9999914219 -0.4473972 -1.0437200
## 610 0.9999914219 -0.1874745 -0.5405530
## 960 0.9999914219 0.5080880 1.2842138
## 1267 0.9999914219 0.4841346 1.0346890
## 176 0.0877508760 -0.9833721 -1.4958055
## 833 0.9999914219 0.5450556 1.3003272
## 343 0.9999914219 -0.1946685 -0.7594478
## 949 0.4298710960 0.4478308 1.4788465
## 287 0.9999914219 0.5726017 1.3009057
## 83 0.9999914219 0.3342495 0.7974118
## 959 0.9999914219 0.4608327 1.2020589
## 401 0.9999914219 -0.3754344 -1.1513542
## 953 0.8646948551 0.4651954 1.3658891
## 1097 0.9999914219 0.1871076 0.5353572
## 349 0.9999914219 0.6094573 0.9926194
## 945 0.9999914219 -0.5797554 -1.3147302
## 687 0.0967986630 -0.9982851 -1.3309732
## 899 0.9999914219 0.3806982 0.8136254
## 1159 0.9999914219 0.4166966 0.7804345
## 35 0.9999914219 0.3096139 0.9866280
## 53 0.9999914219 0.3447694 0.7368386
## 762 0.9999914219 0.4541485 0.8505785
## 853 0.4613736361 -0.9841372 -1.3121103
## 225 0.9999914219 0.3667451 0.7838049
## 1210 0.9999914219 -0.4075661 -0.9976844
## 763 0.9999914219 0.1698824 0.5205860
## 1046 0.9999914219 -0.6834207 -1.2433650
## 1098 0.9999914219 -0.5615586 -1.0852855
## 670 0.1062142586 0.5221956 1.6879851
## 890 0.9999914219 -0.5745717 -1.0453332
## 58 0.8252600322 0.4793273 1.3959003
## 376 0.9999914219 0.4764264 1.0517592
## 519 0.9999914219 0.4762442 1.2422593
## 569 0.9999914219 0.3240139 0.8323880
## 905 0.9999914219 -0.7465872 -1.3582855
## 172 0.9999914219 0.4695651 1.0945944
## 342 0.9999914219 0.5964416 1.1170804
## 526 0.9999914219 0.6221987 1.0948461
## 467 0.9999914219 0.3173976 0.9463849
## 502 0.9999914219 0.4084946 1.0494181
## 1171 0.9999914219 0.4420910 1.1851310
## 237 0.9999914219 0.4658624 0.9591984
## 729 0.9999914219 0.2091121 0.7606287
## 1133 0.9999914219 0.4077576 1.1874745
## 1287 0.9999914219 0.6824769 1.1115461
## 473 0.9999914219 0.3564837 0.7029585
## 1286 0.9999914219 0.2173634 0.7685165
## 773 0.9999914219 0.2871786 0.9696304
## 11 0.9999914219 -0.3935235 -0.6633850
## 399 0.9999914219 0.2473559 0.6792476
## 650 0.9999914219 0.2157351 0.7424691
## 746 0.9999914219 -0.3935235 -0.6633850
## 1052 0.9999914219 -0.3935235 -0.6633850
## 306 0.9999914219 0.5590497 1.1510685
## 256 0.9999914219 0.6946170 1.2222762
## 263 0.9999914219 0.6946170 1.2222762
## 1195 0.9999914219 0.6283007 1.2389607
## 465 0.9999914219 -0.3883995 -0.9060860
## 79 0.9999914219 0.2446287 0.8393483
## 97 0.9999914219 0.2409984 0.8020639
## 427 0.9999914219 0.5691569 1.2163980
## 379 0.9999914219 0.3660250 1.1831674
## 572 0.9999914219 -0.2153758 -0.5678771
## 1337 0.5172212513 -0.9804930 -1.3072517
## 432 0.9999914219 -0.8540193 -1.1386294
## 149 0.9999914219 0.3579849 0.7902877
## 1327 0.9999914219 0.2525142 1.1018178
## 1284 0.1311258251 0.6310363 1.7328471
## 1321 0.9999914219 0.6237190 1.2842211
## 848 0.8257109269 0.5346061 1.4143932
## 1310 0.8257109269 0.7901237 1.3903337
## 570 0.9999914219 -0.4594303 -0.8358534
## 68 0.9999914219 -0.2559537 -0.4946639
## 968 0.9999914219 0.4772727 0.7067703
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## 1293 0.9999914219 -0.1849976 -0.6612918
## 796 0.9999914219 0.2849048 0.8730595
## 50 0.9999914219 -0.7374199 -1.3416070
## 1276 0.9999914219 -0.5257393 -0.9564911
## 1050 0.9999914219 0.2945002 1.1220505
## 185 0.9959058422 0.3492789 1.2288010
## 1 0.9999914219 0.4387532 0.8651876
## 732 0.9999914219 0.2570190 0.8845510
## 931 0.1472479558 0.8153829 1.6788519
## 321 0.9999914219 0.3353399 0.6904560
## 87 0.9999914219 0.3420664 0.7973846
## 451 0.9999914219 0.3565732 0.8101062
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## 1324 0.9999914219 0.3420664 0.7973846
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## 844 0.9999914219 0.8212219 1.0949019
## 289 0.9999914219 0.2663798 0.8687720
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## 891 0.9999914219 -0.4324348 -1.1744019
## 365 0.8980476784 -0.4513606 -1.3512714
## 933 0.9999914219 0.3042476 0.8623170
## 679 0.6356681642 0.6944236 1.4841172
## 700 0.9999914219 -0.2683182 -0.7890669
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## 135 3785036for(curr_pathway in fgsea_ms_total_protein_reactome_table$pathway[1:6]){make_protein_pathway_plots(curr_pathway,category='significant')}## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\MS_total_protein\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \MS_total_protein\significant\REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\MS_total_protein\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
## category, "/", : 'Y:\DUX4_multiomics\Analysis\fGSEA_res\
## \MS_total_protein\significant\REACTOME_ACTIVATION_OF_ANTERIOR_HOX_GENES_IN_HINDBRAIN_DEVELOPMENT_DURING_EARLY_EMBRYOGENESIS'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\MS_total_protein\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
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## \MS_total_protein\significant\REACTOME_B_WICH_COMPLEX_POSITIVELY_REGULATES_RRNA_EXPRESSION'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\MS_total_protein\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
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## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
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## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
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## \MS_total_protein\significant\REACTOME_BASE_EXCISION_REPAIR_AP_SITE_FORMATION'
## already exists
## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/", category, "/")):
## 'Y:\DUX4_multiomics\Analysis\fGSEA_res\\MS_total_protein\significant' already
## exists## Warning in dir.create(paste0(GSEA_DIR, "/MS_total_protein/",
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## \MS_total_protein\significant\REACTOME_CELLULAR_SENESCENCE' already exists
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('DAMAGE',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('SPLIC',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}
#for(curr_pathway in fgsea_RNAseq_lengthScaled_reactome_table$pathway[grep('SENESC',fgsea_RNAseq_lengthScaled_reactome_table$pathway)]){make_pathway_plots(curr_pathway,category='other')}intersect(fgsea_ms_total_protein_reactome_table$pathway[fgsea_ms_total_protein_reactome_table$padj_iDUX4t14dox_iDUX4dmso<0.05 & fgsea_ms_total_protein_reactome_table$padj_iDUX4t06dox_iDUX4dmso<0.05 & fgsea_ms_total_protein_reactome_table$padj_WTt14dox_WTdmso>0.05], fgsea_RNAseq_lengthScaled_reactome_table$pathway[fgsea_RNAseq_lengthScaled_reactome_table$padj_iDUX4t14dox_iDUX4dmso<0.05 & fgsea_RNAseq_lengthScaled_reactome_table$padj_WTt14dox_WTdmso>0.05])## [1] "REACTOME_HDACS_DEACETYLATE_HISTONES"
## [2] "REACTOME_HDMS_DEMETHYLATE_HISTONES"
## [3] "REACTOME_ESR_MEDIATED_SIGNALING"
## [4] "REACTOME_HATS_ACETYLATE_HISTONES"
## [5] "REACTOME_HCMV_LATE_EVENTS"
## [6] "REACTOME_SIGNALING_BY_NUCLEAR_RECEPTORS"
## [7] "REACTOME_HCMV_EARLY_EVENTS"
## [8] "REACTOME_HCMV_INFECTION"Comparison with Jagannathan 2016/2019
RNA-seq from https://pubmed.ncbi.nlm.nih.gov/28171552/, proteomics from https://pubmed.ncbi.nlm.nih.gov/30644821/
Jagannathan_RNAseq = as.data.frame(read_delim('C:/Users/hilla34/OneDrive - Pfizer/Documents/Projects/DUX4_FSHD/Jagannathan_supplement/ddw271_supp/Jagannathan-Suppl-Table1.txt',delim='\t'))## Rows: 21042 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): Ensembl ID, genes
## dbl (15): iDUX4_logFC, iDUX4_logCPM, iDUX4_LR, iDUX4_pval, iDUX4_fdr, enDUX4...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Jagannathan_iDUX4_RNAseq = Jagannathan_RNAseq[,c(1:2,grep('iDUX4',colnames(Jagannathan_RNAseq)))]
merged_Jagannathan_Brennan_RNAseq = merge(Jagannathan_iDUX4_RNAseq,limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_iDUX4dmso,by.x='Ensembl ID',by.y='Row.names',all=FALSE)
ggplot(merged_Jagannathan_Brennan_RNAseq,aes(x=iDUX4_logFC,y=log2FC,color = log10(reference_mean+0.5))) + geom_point(alpha = 0.5) + xlab('Jagannathan 2016') + ylab('present data, iDUX4 t14 vs DMSO')
ggsave('Y:/DUX4_multiomics/Analysis/Jagannathan_comparison/RNAseq.pdf',device='pdf',width=10,height=10)
Jagannathan_2019 = as.data.frame(read_delim('Y:/DUX4_multiomics/Analysis/Jagannathan_comparison/Jagannathan 2019 data.txt',delim='\t'))## Rows: 21282 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): geneid
## dbl (3): iDUX4_logFC, iDUX4_logFC_UF, hl.ratio
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Jagannathan_2019_RNAseq_merged = merge(limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_iDUX4dmso,Jagannathan_2019[,c('geneid','iDUX4_logFC')],by.x='external_gene_name',by.y='geneid',all=FALSE)
ggplot(Jagannathan_2019_RNAseq_merged,aes(x=iDUX4_logFC,y=log2FC,color = log10(reference_mean+0.5))) + geom_point(alpha = 0.5) + xlab('Jagannathan 2019') + ylab('present data, iDUX4 t14 vs DMSO')## Warning: Removed 2946 rows containing missing values (geom_point).
ggsave('Y:/DUX4_multiomics/Analysis/Jagannathan_comparison/RNAseq.pdf',device='pdf',width=10,height=10)## Warning: Removed 2946 rows containing missing values (geom_point).cor(Jagannathan_2019_RNAseq_merged$iDUX4_logFC, Jagannathan_2019_RNAseq_merged$log2FC,method='pearson',use='pairwise.complete.obs')## [1] 0.9231956Jagannathan_2019_MS_merged = merge(ms_total_protein_conditions_contrasts$iDUX4t14dox_iDUX4dmso,Jagannathan_2019[,c('geneid','hl.ratio')],by.x='hgnc_symbol',by.y='geneid',all=FALSE)
ggplot(Jagannathan_2019_MS_merged,aes(x=hl.ratio,y=log2FC,color = reference_mean)) + geom_point(alpha = 0.5) + xlab('Jagannathan 2019') + ylab('present data, iDUX4 t14 vs DMSO')## Warning: Removed 876 rows containing missing values (geom_point).
ggsave('Y:/DUX4_multiomics/Analysis/Jagannathan_comparison/MS.pdf',device='pdf',width=10,height=10)## Warning: Removed 876 rows containing missing values (geom_point).cor(Jagannathan_2019_MS_merged$hl.ratio, Jagannathan_2019_MS_merged$log2FC,method='pearson',use='pairwise.complete.obs')## [1] 0.3086512MAJIQ splicing
MAJIQ_contrasts = gsub('.deltapsi.tsv','',list.files(paste0(MAJIQ_DIR,'/dpsi_out/'),pattern='.deltapsi.tsv'),fixed=TRUE)
MAJIQ_tsv = list()
MAJIQ_dpsi_contrasts = list()
for(contrast_idx in 1:length(MAJIQ_contrasts)){
curr_contrast = MAJIQ_contrasts[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][2]
curr_numerator = strsplit(curr_contrast,'_')[[1]][2]
# curr_condition_means = cpm_condition_means[,c(curr_numerator,curr_reference)]
#colnames(curr_condition_means) = c('numerator_mean','reference_mean')
MAJIQ_tsv[[contrast_idx]] = read.table(paste0(MAJIQ_DIR,'dpsi_out/',curr_contrast,'.deltapsi.tsv'),sep='\t',header=TRUE)
MAJIQ_dpsi_contrasts[[contrast_idx]] = read.table(paste0(MAJIQ_DIR,'dpsi_out/',curr_contrast,'.deltapsi.split.tsv'),sep='\t',header=TRUE)
}
names(MAJIQ_dpsi_contrasts) = MAJIQ_contrasts
names(MAJIQ_tsv) = MAJIQ_contrasts
for(contrast_idx in 1:length(MAJIQ_contrasts)){
curr_contrast = MAJIQ_contrasts[contrast_idx]
curr_reference = strsplit(curr_contrast,'_')[[1]][1]
ggplot(MAJIQ_dpsi_contrasts[[curr_contrast]],aes(x=E.dPSI..per.LSV.junction,y=P..dPSI...0.20..per.LSV.junction,color=is_IR)) + geom_point(alpha=0.5,shape=16) + ggtitle(paste('MAJIQ dPSI:',curr_contrast)) + xlab('E(dPSI)') + ylab('Prob dPSI > 20%') + scale_color_manual(values = c("#008837","#7b3294"),name='Intron Retention') + theme_bw()
#ggsave(paste('Y:/DUX4_multiomics/Analysis/volcano_plots_lessthan1/',curr_contrast,'.pdf',sep=''),device='pdf',width=10,height=8)
}MAJIQ barplots
MAJIQ_conditions_to_plot = c('iDUX4t00_iDUX4t02','iDUX4t00_iDUX4t06','iDUX4t00_iDUX4t14','WTt00_WTt14')
dPSI20_counts = unlist(lapply(MAJIQ_dpsi_contrasts,function(x) sum(x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction>0)))
dPSI20_IR_counts = unlist(lapply(MAJIQ_dpsi_contrasts,function(x) sum(x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction>0 & x$is_IR==TRUE)))
dPSI20_IR_percent = dPSI20_IR_counts/dPSI20_counts
barplot(dPSI20_counts,las=3,main='Junction count dPSI>20, increased')
barplot(dPSI20_IR_counts,las=3,main='Intron retention count dPSI>20, increased')
barplot(dPSI20_IR_percent,las=3,main='Fraction junction counts with dPSI>20 that are intron retention')
dPSI20_counts = unlist(lapply(MAJIQ_dpsi_contrasts,function(x) sum(x$P..dPSI...0.20..per.LSV.junction>0.95)))
dPSI20_IR_counts = unlist(lapply(MAJIQ_dpsi_contrasts,function(x) sum(x$P..dPSI...0.20..per.LSV.junction>0.95 & x$is_IR==TRUE)))
dPSI20_IR_percent = dPSI20_IR_counts/dPSI20_counts
barplot(dPSI20_counts,las=3,main='Junction count dPSI>20')
barplot(dPSI20_IR_counts,ylim=c(0,200),las=3,main='Intron retention count dPSI>20')
barplot(dPSI20_IR_percent,las=3,main='Fraction junction counts with dPSI>20 that are intron retention')
dPSI20_counts_to_plot = as.data.frame(dPSI20_counts[names(dPSI20_counts) %in% MAJIQ_conditions_to_plot])
dPSI20_counts_to_plot$condition = row.names(dPSI20_counts_to_plot)
colnames(dPSI20_counts_to_plot) = c('counts','condition')
ggplot(dPSI20_counts_to_plot,aes(condition,counts)) + geom_col() + theme(axis.text.x = element_text(angle = 90)) + ylab('LSV Junction Count') + geom_text(aes(label=counts),vjust=-0.25,size=4)
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/dPSI20_junction_counts.pdf',device='pdf',width=8,height=8)
IR_dPSI20_counts = lapply(MAJIQ_dpsi_contrasts,function(x) c(sum(x$is_IR & x$P..dPSI...0.20..per.LSV.junction<=0.95), sum(x$is_IR & x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction>0),sum(x$is_IR & x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction<0)))
IR_dPSI20_counts_table = as.data.frame(unlist(IR_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3-2])
rownames(IR_dPSI20_counts_table) = names(IR_dPSI20_counts)
IR_dPSI20_counts_table$increased = unlist(IR_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3-1]
IR_dPSI20_counts_table$decreased = unlist(IR_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3]
colnames(IR_dPSI20_counts_table) = c('unchanged','increased','decreased')
IR_dPSI20_counts_table$condition = row.names(IR_dPSI20_counts_table)
# select the "comparisons of interest" only
IR_dPSI20_counts_to_plot = IR_dPSI20_counts_table[c(1:3,5),c(2:4)]
ggplot(melt(IR_dPSI20_counts_to_plot),aes(condition,value,fill=variable)) + geom_col() + theme(axis.text.x = element_text(angle = 90)) + ylab('LSV Junction Count')## Using condition as id variables
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/intron_retention.pdf',device='pdf',width=8,height=8)
ES_dPSI20_counts = lapply(MAJIQ_dpsi_contrasts,function(x) c(sum(x$exons_skipped>0 & x$P..dPSI...0.20..per.LSV.junction<=0.95), sum(x$exons_skipped>0 & x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction>0),sum(x$exons_skipped>0 & x$P..dPSI...0.20..per.LSV.junction>0.95 & x$E.dPSI..per.LSV.junction<0)))
ES_dPSI20_counts_table = as.data.frame(unlist(ES_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3-2])
rownames(ES_dPSI20_counts_table) = names(ES_dPSI20_counts)
ES_dPSI20_counts_table$increased = unlist(ES_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3-1]
ES_dPSI20_counts_table$decreased = unlist(ES_dPSI20_counts)[c(1:length(MAJIQ_contrasts))*3]
colnames(ES_dPSI20_counts_table) = c('unchanged','increased','decreased')
ES_dPSI20_counts_table$condition = row.names(ES_dPSI20_counts_table)
# select the "comparisons of interest" only
ES_dPSI20_counts_to_plot = ES_dPSI20_counts_table[c(1:3,5),c(2:4)]
ggplot(melt(ES_dPSI20_counts_to_plot),aes(condition,value,fill=variable)) + geom_col() + theme(axis.text.x = element_text(angle = 90)) + ylab('LSV Junction Count')## Using condition as id variables
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/exon_skipping.pdf',device='pdf',width=8,height=8)
dPSI20_gene_counts = as.data.frame(unlist(lapply(MAJIQ_dpsi_contrasts,function(x) length(unique(x[x$P..dPSI...0.20..per.LSV.junction>0.95,]$Gene.ID)))))
dPSI20_gene_counts$condition = row.names(dPSI20_gene_counts)
colnames(dPSI20_gene_counts) = c('gene_count','condition')
ggplot(dPSI20_gene_counts[c(1:3,5),],aes(condition,gene_count)) + geom_col() + geom_text(aes(label=gene_count),vjust=-0.25,size=4) + ylab('Number of Genes with Significant LSV')
ggsave('Y:/DUX4_multiomics/Analysis/splicing_plots/gene_count_significant_events.pdf',device='pdf',width=8,height=8)Cross-’omics comparisons
Protein abundance and splicing
Is there overlap in differential protein abundance and splicing changes?
intersect(unique(MAJIQ_dpsi_contrasts$iDUX4t00_iDUX4t14$external_gene_name[MAJIQ_dpsi_contrasts$iDUX4t00_iDUX4t14$P..dPSI...0.20..per.LSV.junction>0.95]), ms_total_protein_conditions_contrasts$iDUX4t14dox_iDUX4dmso$Gene[ms_total_protein_conditions_contrasts$iDUX4t14dox_iDUX4dmso$adj_pvalue<0.1])## [1] "ZNF638" "TGFB2" "DBR1" "ZNF217" "SPG7"RNA and protein
head(total_protein_comparisons$iDUX4t14dox_iDUX4dmso)## Protein Gene Description ProteinGroup
## 1 P40222 TXLNA Alpha-taxilin P40222
## 2 Q8NBS9 TXNDC5 Thioredoxin domain-containing protein 5 Q8NBS9
## 3 O14530 TXNDC9 Thioredoxin domain-containing protein 9 O14530
## 4 O43396 TXNL1 Thioredoxin-like protein 1 O43396
## 5 Q16563 SYPL1 Synaptophysin-like protein 1 Q16563
## 6 P47897 QARS Glutamine--tRNA ligase P47897
## log2FC adj_pvalue pvalue
## 1 -0.08021061 0.9048223 0.2716738
## 2 -0.01051217 0.9906133 0.9036854
## 3 -0.18985874 0.8334083 0.1551630
## 4 0.11339369 0.7120630 0.0616433
## 5 0.08881894 0.9450211 0.6169264
## 6 0.07102547 0.9259508 0.3539390head(limma_res_lengthScaled_conditions_contrasts$iDUX4t14dox_iDUX4dmso)## Row.names log2FC AveExpr t P.Value adj_pvalue
## 1 caDUX4_sequence 8.62217451 1.941414 33.4410293 1.593238e-21 6.212249e-20
## 2 ENSG00000000003 0.02890418 4.758355 0.2320725 8.184649e-01 8.545630e-01
## 3 ENSG00000000419 0.95686464 5.054309 10.6442090 1.569432e-10 7.052899e-10
## 4 ENSG00000000457 1.56161647 3.203081 17.9053584 2.571499e-15 2.541836e-14
## 5 ENSG00000000460 -0.05151938 3.879153 -0.5840386 5.646874e-01 6.311114e-01
## 6 ENSG00000000971 0.86550969 7.175606 9.1097144 3.159315e-09 1.167237e-08
## B numerator_mean reference_mean ensembl_gene_id_version
## 1 39.406187 224.54924 0.5522552 caDUX4_sequence
## 2 -3.150934 30.05726 29.8187635 ENSG00000000003.15
## 3 18.365104 56.53990 29.3180561 ENSG00000000419.12
## 4 29.746293 21.99483 7.4650823 ENSG00000000457.14
## 5 -2.790102 14.87838 15.5626079 ENSG00000000460.17
## 6 14.994784 225.81310 123.9663307 ENSG00000000971.15
## external_gene_name gene_biotype strand chromosome_name
## 1 caDUX4 inducible_protein_coding NA NA
## 2 TSPAN6 protein_coding -1 X
## 3 DPM1 protein_coding -1 20
## 4 SCYL3 protein_coding -1 1
## 5 C1orf112 protein_coding 1 1
## 6 CFH protein_coding 1 1
## start_position end_position band
## 1 NA NA NA
## 2 100627108 100639991 q22.1
## 3 50934867 50958555 q13.13
## 4 169849631 169894267 q24.2
## 5 169662007 169854080 q24.2
## 6 196651878 196747504 q31.3
## description
## 1 inducible codon altered DUX4
## 2 tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]
## 3 dolichyl-phosphate mannosyltransferase subunit 1, catalytic [Source:HGNC Symbol;Acc:HGNC:3005]
## 4 SCY1 like pseudokinase 3 [Source:HGNC Symbol;Acc:HGNC:19285]
## 5 chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:HGNC:25565]
## 6 complement factor H [Source:HGNC Symbol;Acc:HGNC:4883]merged_RNA_protein=list()
for(contrast_idx in 1:length(rownames(model_contrasts_of_interest))){
merged_RNA_protein[[contrast_idx]] = merge(limma_res_lengthScaled_conditions_contrasts[[contrast_idx]],total_protein_comparisons[[contrast_idx]],by.x='external_gene_name',by.y='Gene',all=FALSE,suffixes=c('_RNA','_protein'))
merged_RNA_protein[[contrast_idx]]$significant_analytes = 'neither'
merged_RNA_protein[[contrast_idx]]$significant_analytes[merged_RNA_protein[[contrast_idx]]$adj_pvalue_RNA<0.05] = 'RNA only'
merged_RNA_protein[[contrast_idx]]$significant_analytes[merged_RNA_protein[[contrast_idx]]$adj_pvalue_protein<0.05] = 'protein only'
merged_RNA_protein[[contrast_idx]]$significant_analytes[merged_RNA_protein[[contrast_idx]]$adj_pvalue_RNA<0.05 & merged_RNA_protein[[contrast_idx]]$adj_pvalue_protein<0.05] = 'RNA and protein'
}
names(merged_RNA_protein) = rownames(model_contrasts_of_interest)
RNA_log2FC_lim = 1.01*max(abs(unlist(lapply(merged_RNA_protein,function(x) range(x$log2FC_RNA,na.rm=TRUE,finite=TRUE)))))
protein_log2FC_lim = 1.01*max(abs(unlist(lapply(merged_RNA_protein,function(x) range(x$log2FC_protein,na.rm=TRUE,finite=TRUE)))))
contrast_idx = 2
ggplotly(ggplot(merged_RNA_protein[[contrast_idx]],aes(x=log2FC_RNA,y=log2FC_protein,color=significant_analytes,label=external_gene_name)) + geom_point(alpha=0.5) + ggtitle(paste(rownames(model_contrasts_of_interest)[contrast_idx])) + xlim(RNA_log2FC_lim*c(-1,1)) + ylim(protein_log2FC_lim*c(-1,1)))ggplot(merged_RNA_protein[[contrast_idx]],aes(x=log2FC_RNA,y=log2FC_protein,color=significant_analytes,label=external_gene_name)) + geom_point(alpha=0.5) + ggtitle(paste(rownames(model_contrasts_of_interest)[contrast_idx])) + xlim(RNA_log2FC_lim*c(-1,1)) + ylim(protein_log2FC_lim*c(-1,1)) + geom_text_repel(max.overlaps=8)## Warning: ggrepel: 4446 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
table(merged_RNA_protein[[contrast_idx]]$significant_analytes)##
## neither protein only RNA and protein RNA only
## 621 3 12 3811contrast_idx = 5
ggplotly(ggplot(merged_RNA_protein[[contrast_idx]],aes(x=log2FC_RNA,y=log2FC_protein,color=significant_analytes,label=external_gene_name)) + geom_point(alpha=0.5) + ggtitle(paste(rownames(model_contrasts_of_interest)[contrast_idx])) + xlim(RNA_log2FC_lim*c(-1,1)) + ylim(protein_log2FC_lim*c(-1,1)))ggplot(merged_RNA_protein[[contrast_idx]],aes(x=log2FC_RNA,y=log2FC_protein,color=significant_analytes,label=external_gene_name)) + geom_point(alpha=0.5) + ggtitle(paste(rownames(model_contrasts_of_interest)[contrast_idx])) + xlim(RNA_log2FC_lim*c(-1,1)) + ylim(protein_log2FC_lim*c(-1,1)) + geom_text_repel(max.overlaps=8)## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text_repel).## Warning: ggrepel: 4443 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
table(merged_RNA_protein[[contrast_idx]]$significant_analytes)##
## neither protein only RNA and protein RNA only
## 627 10 137 3673RNA, protein, and splicing
contrast_idx = 5
ggplotly(ggplot(merged_RNA_protein[[contrast_idx]],aes(x=log2FC_RNA,y=log2FC_protein,color=external_gene_name %in% unique(MAJIQ_dpsi_contrasts$iDUX4t00_iDUX4t14$external_gene_name[MAJIQ_dpsi_contrasts$iDUX4t00_iDUX4t14$P..dPSI...0.20..per.LSV.junction>0.95]),label=external_gene_name)) + geom_point(alpha=0.5) + ggtitle(paste(rownames(model_contrasts_of_interest)[contrast_idx])) + xlim(RNA_log2FC_lim*c(-1,1)) + ylim(protein_log2FC_lim*c(-1,1)) + labs(color='P(dPSI>20%)>95%'))Save results
RNAseq_comparisons_res = list(RNA_genes = limma_res_lengthScaled_conditions_contrasts,RNA_transcripts=transcript_limma_res_lengthScaled_conditions_contrasts)
saveRDS(RNAseq_comparisons_res,'Y:/DUX4_multiomics/Analysis/DUX4_multiomics_viewer/data/comparisons_res.rds')
MS_comparisons_res = list(total_protein = total_protein_comparisons, total_protein_peptides = total_protein_peptide_comparisons, phospho_protein_peptides = phospho_peptide_comparisons)
saveRDS(MS_comparisons_res,'Y:/DUX4_multiomics/Analysis/DUX4_multiomics_viewer/data/MS_comparisons_res.rds')
MS_quants_res = list(total_protein = total_protein_quantwide, total_protein_peptides = total_protein_quantwide_peptides,phospho_protein_peptides = phospho_quantwide_peptides)
saveRDS(MS_quants_res,'Y:/DUX4_multiomics/Analysis/DUX4_multiomics_viewer/data/MS_quants_res.rds')Session Info
sessionInfo()## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggrepel_0.9.1 reshape2_1.4.4
## [3] pheatmap_1.0.12 NMF_0.24.0
## [5] cluster_2.1.3 rngtools_1.5.2
## [7] pkgmaker_0.32.2 registry_0.5-1
## [9] scales_1.2.0 plotly_4.10.0
## [11] httr_1.4.3 RColorBrewer_1.1-3
## [13] fgsea_1.22.0 apeglm_1.18.0
## [15] rtracklayer_1.56.0 biomaRt_2.52.0
## [17] devtools_2.4.3 usethis_2.1.6
## [19] gridExtra_2.3 sva_3.44.0
## [21] BiocParallel_1.30.2 genefilter_1.78.0
## [23] mgcv_1.8-40 nlme_3.1-157
## [25] swamp_1.5.1 MASS_7.3-57
## [27] gplots_3.1.3 amap_0.8-18
## [29] impute_1.70.0 edgeR_3.38.1
## [31] limma_3.52.1 DESeq2_1.36.0
## [33] SummarizedExperiment_1.26.1 Biobase_2.56.0
## [35] MatrixGenerics_1.8.0 matrixStats_0.62.0
## [37] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
## [39] IRanges_2.30.0 S4Vectors_0.34.0
## [41] BiocGenerics_0.42.0 tximport_1.24.0
## [43] forcats_0.5.1 stringr_1.4.0
## [45] dplyr_1.0.9 purrr_0.3.4
## [47] readr_2.1.2 tidyr_1.2.0
## [49] tibble_3.1.7 ggplot2_3.3.6
## [51] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 RSQLite_2.2.14
## [4] AnnotationDbi_1.58.0 htmlwidgets_1.5.4 grid_4.2.0
## [7] munsell_0.5.0 codetools_0.2-18 withr_2.5.0
## [10] colorspace_2.0-3 filelock_1.0.2 highr_0.9
## [13] knitr_1.39 rstudioapi_0.13 labeling_0.4.2
## [16] bbmle_1.0.25 GenomeInfoDbData_1.2.8 farver_2.1.0
## [19] bit64_4.0.5 rprojroot_2.0.3 coda_0.19-4
## [22] vctrs_0.4.1 generics_0.1.2 xfun_0.31
## [25] BiocFileCache_2.4.0 R6_2.5.1 doParallel_1.0.17
## [28] locfit_1.5-9.5 bitops_1.0-7 cachem_1.0.6
## [31] DelayedArray_0.22.0 assertthat_0.2.1 vroom_1.5.7
## [34] BiocIO_1.6.0 gtable_0.3.0 processx_3.5.3
## [37] rlang_1.0.2 splines_4.2.0 lazyeval_0.2.2
## [40] broom_0.8.0 yaml_2.3.5 modelr_0.1.8
## [43] crosstalk_1.2.0 backports_1.4.1 tools_4.2.0
## [46] gridBase_0.4-7 ellipsis_0.3.2 jquerylib_0.1.4
## [49] sessioninfo_1.2.2 Rcpp_1.0.8.3 plyr_1.8.7
## [52] progress_1.2.2 zlibbioc_1.42.0 RCurl_1.98-1.6
## [55] ps_1.7.0 prettyunits_1.1.1 haven_2.5.0
## [58] fs_1.5.2 magrittr_2.0.3 data.table_1.14.2
## [61] reprex_2.0.1 mvtnorm_1.1-3 pkgload_1.2.4
## [64] hms_1.1.1 evaluate_0.15 xtable_1.8-4
## [67] XML_3.99-0.9 emdbook_1.3.12 readxl_1.4.0
## [70] testthat_3.1.4 compiler_4.2.0 bdsmatrix_1.3-4
## [73] KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.2
## [76] tzdb_0.3.0 geneplotter_1.74.0 lubridate_1.8.0
## [79] DBI_1.1.3 dbplyr_2.2.0 rappdirs_0.3.3
## [82] Matrix_1.4-1 brio_1.1.3 cli_3.3.0
## [85] pkgconfig_2.0.3 GenomicAlignments_1.32.0 numDeriv_2016.8-1.1
## [88] xml2_1.3.3 foreach_1.5.2 annotate_1.74.0
## [91] bslib_0.3.1 XVector_0.36.0 rvest_1.0.2
## [94] callr_3.7.0 digest_0.6.29 Biostrings_2.64.0
## [97] rmarkdown_2.14 cellranger_1.1.0 fastmatch_1.1-3
## [100] restfulr_0.0.13 curl_4.3.2 Rsamtools_2.12.0
## [103] gtools_3.9.2.1 rjson_0.2.21 lifecycle_1.0.1
## [106] jsonlite_1.8.0 desc_1.4.1 viridisLite_0.4.0
## [109] fansi_1.0.3 pillar_1.7.0 lattice_0.20-45
## [112] KEGGREST_1.36.2 fastmap_1.1.0 pkgbuild_1.3.1
## [115] survival_3.3-1 glue_1.6.2 remotes_2.4.2
## [118] png_0.1-7 iterators_1.0.14 bit_4.0.4
## [121] stringi_1.7.6 sass_0.4.1 blob_1.2.3
## [124] caTools_1.18.2 memoise_2.0.1